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2025-05-14 13:10:40 ·

Congress proposes 10-year ban on state AI regulations

House Republicans have proposed banning states from regulating AI for the next ten years. The sweeping moratorium, quietly tucked into the Budget Reconciliation Bill last Sunday, would block most state and local governments from enforcing AI regulations until 2035 if passed.

The proposed legislation stated that “no State or political subdivision thereof may enforce any law or regulation regulating artificial intelligence models, artificial intelligence systems, or automated decision systems,” for 10 years.

Industry experts warn that this potential regulatory vacuum would come precisely when AI systems are becoming more powerful and pervasive across the US society.

Oversight gap raises concerns

The moratorium would create an unprecedented situation: rapidly evolving AI technology would operate without state-level guardrails during what may be its most transformative decade.

“The proposed decade-long moratorium on state-level AI regulations presents a double-edged sword,” said Abhivyakti Sengar, practice director at Everest Group. “On one hand, it aims to prevent a fragmented regulatory environment that could stifle innovation, on the other hand, it risks creating a regulatory vacuum, leaving critical decisions about AI governance in the hands of private entities without sufficient oversight.”

The proposed legislation includes specific exceptions. According to the bill text, states would still be allowed to enforce laws that have “the primary purpose and effect of which is to remove legal impediments to, or facilitate the deployment or operation of, an artificial intelligence model, artificial intelligence system, or automated decision system.”

States could also enforce laws that streamline “licensing, permitting, routing, zoning, procurement, or reporting procedures” for AI systems.

However, the bill explicitly prohibits states from imposing “any substantive design, performance, data-handling, documentation, civil liability, taxation, fee, or other requirement” specifically on AI unless such requirements are applied equally to non-AI systems with similar functions.

This limitation would prevent states from creating AI-specific oversight frameworks that address the technology’s unique capabilities and risks.

State AI regulations threatened

If enacted, the impact could be significant. Several states have been developing AI oversight frameworks that would likely become unenforceable under the federal provision.

Various state-level efforts to regulate AI systems — from algorithmic transparency requirements to data privacy protections for AI training — could be effectively neutralized without public debate or input.

The moratorium particularly threatens state data privacy protections. Without these state laws, consumers have few guarantees regarding how AI systems use their data, obtain consent, or make decisions affecting their lives.

Global standards diverge

The US approach now stands in stark contrast to the European Union’s comprehensive AI Act, which imposes strict requirements on high-risk AI systems.

“As the US diverges from the EU’s stringent AI regulatory framework, multinational enterprises may face the challenge of navigating conflicting standards,” Sengar noted. This divergence potentially leads to “increased compliance costs and operational complexities.”

Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research, sees a splintering global AI landscape ahead.

“America’s moratorium will likely deepen the regulatory divergence with Europe,” said Gogia. “This will accelerate the fragmentation of global AI product design, where use-case eligibility and ethical thresholds vary dramatically by geography.”

Enterprises face a new reality

For businesses, the regulatory clarity comes with difficult strategic decisions. Companies must determine how aggressively to implement AI systems during this regulation-free decade.

Many large companies aren’t waiting for government guidance. “Even before public oversight being put on hold, large enterprises have already launched internal AI governance councils,” Gogia explained. “These internal regimes — led by CISOs, legal, and risk teams — are becoming the primary referees for responsible AI use.”

But Gogia cautioned against over-reliance on self-regulation: “While these structures are necessary, they are not a long-term substitute for statutory accountability.”

Legal uncertainty remains

Despite the moratorium on regulations, experts warn that companies still face significant liability risks.

“The absence of clear legal guidelines could result in heightened legal uncertainty, as courts grapple with AI-related disputes without established precedents,” said Sengar.

Gogia puts it more bluntly: “Even in a regulatory freeze, enterprises remain legally accountable. I believe the lack of specific laws does not eliminate legal exposure — it merely shifts the battleground from compliance desks to courtrooms.”

While restricting state action, the legislation simultaneously expands the federal government’s AI footprint. The bill allocates million to the Department of Commerce for AI modernization through 2035.

The money targets legacy system replacement, operational efficiency improvements, and cybersecurity enhancements using AI technologies.

This dual approach positions the federal government as both the primary AI regulator and a major AI customer, consolidating tremendous influence over the technology’s direction.

Finding balance

Industry observers emphasize the need for thoughtful governance despite the moratorium.

“In this rapidly evolving landscape, a balanced approach that fosters innovation while ensuring accountability and public trust is paramount,” Sengar noted. Gogia offers a succinct assessment of the situation: “The 10-year moratorium on US state and local AI regulation removes complexity but not risk. I believe innovation does need room, but room without direction risks misalignment between corporate ethics and public interest.”
#congress #proposes #10year #ban #state

Congress proposes 10-year ban on state AI regulations
House Republicans have proposed banning states from regulating AI for the next ten years. The sweeping moratorium, quietly tucked into the Budget Reconciliation Bill last Sunday, would block most state and local governments from enforcing AI regulations until 2035 if passed. The proposed legislation stated that “no State or political subdivision thereof may enforce any law or regulation regulating artificial intelligence models, artificial intelligence systems, or automated decision systems,” for 10 years. Industry experts warn that this potential regulatory vacuum would come precisely when AI systems are becoming more powerful and pervasive across the US society. Oversight gap raises concerns The moratorium would create an unprecedented situation: rapidly evolving AI technology would operate without state-level guardrails during what may be its most transformative decade. “The proposed decade-long moratorium on state-level AI regulations presents a double-edged sword,” said Abhivyakti Sengar, practice director at Everest Group. “On one hand, it aims to prevent a fragmented regulatory environment that could stifle innovation, on the other hand, it risks creating a regulatory vacuum, leaving critical decisions about AI governance in the hands of private entities without sufficient oversight.” The proposed legislation includes specific exceptions. According to the bill text, states would still be allowed to enforce laws that have “the primary purpose and effect of which is to remove legal impediments to, or facilitate the deployment or operation of, an artificial intelligence model, artificial intelligence system, or automated decision system.” States could also enforce laws that streamline “licensing, permitting, routing, zoning, procurement, or reporting procedures” for AI systems. However, the bill explicitly prohibits states from imposing “any substantive design, performance, data-handling, documentation, civil liability, taxation, fee, or other requirement” specifically on AI unless such requirements are applied equally to non-AI systems with similar functions. This limitation would prevent states from creating AI-specific oversight frameworks that address the technology’s unique capabilities and risks. State AI regulations threatened If enacted, the impact could be significant. Several states have been developing AI oversight frameworks that would likely become unenforceable under the federal provision. Various state-level efforts to regulate AI systems — from algorithmic transparency requirements to data privacy protections for AI training — could be effectively neutralized without public debate or input. The moratorium particularly threatens state data privacy protections. Without these state laws, consumers have few guarantees regarding how AI systems use their data, obtain consent, or make decisions affecting their lives. Global standards diverge The US approach now stands in stark contrast to the European Union’s comprehensive AI Act, which imposes strict requirements on high-risk AI systems. “As the US diverges from the EU’s stringent AI regulatory framework, multinational enterprises may face the challenge of navigating conflicting standards,” Sengar noted. This divergence potentially leads to “increased compliance costs and operational complexities.” Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research, sees a splintering global AI landscape ahead. “America’s moratorium will likely deepen the regulatory divergence with Europe,” said Gogia. “This will accelerate the fragmentation of global AI product design, where use-case eligibility and ethical thresholds vary dramatically by geography.” Enterprises face a new reality For businesses, the regulatory clarity comes with difficult strategic decisions. Companies must determine how aggressively to implement AI systems during this regulation-free decade. Many large companies aren’t waiting for government guidance. “Even before public oversight being put on hold, large enterprises have already launched internal AI governance councils,” Gogia explained. “These internal regimes — led by CISOs, legal, and risk teams — are becoming the primary referees for responsible AI use.” But Gogia cautioned against over-reliance on self-regulation: “While these structures are necessary, they are not a long-term substitute for statutory accountability.” Legal uncertainty remains Despite the moratorium on regulations, experts warn that companies still face significant liability risks. “The absence of clear legal guidelines could result in heightened legal uncertainty, as courts grapple with AI-related disputes without established precedents,” said Sengar. Gogia puts it more bluntly: “Even in a regulatory freeze, enterprises remain legally accountable. I believe the lack of specific laws does not eliminate legal exposure — it merely shifts the battleground from compliance desks to courtrooms.” While restricting state action, the legislation simultaneously expands the federal government’s AI footprint. The bill allocates million to the Department of Commerce for AI modernization through 2035. The money targets legacy system replacement, operational efficiency improvements, and cybersecurity enhancements using AI technologies. This dual approach positions the federal government as both the primary AI regulator and a major AI customer, consolidating tremendous influence over the technology’s direction. Finding balance Industry observers emphasize the need for thoughtful governance despite the moratorium. “In this rapidly evolving landscape, a balanced approach that fosters innovation while ensuring accountability and public trust is paramount,” Sengar noted. Gogia offers a succinct assessment of the situation: “The 10-year moratorium on US state and local AI regulation removes complexity but not risk. I believe innovation does need room, but room without direction risks misalignment between corporate ethics and public interest.” #congress #proposes #10year #ban #state

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Congress proposes 10-year ban on state AI regulations

House Republicans have proposed banning states from regulating AI for the next ten years. The sweeping moratorium, quietly tucked into the Budget Reconciliation Bill last Sunday, would block most state and local governments from enforcing AI regulations until 2035 if passed. The proposed legislation stated that “no State or political subdivision thereof may enforce any law or regulation regulating artificial intelligence models, artificial intelligence systems, or automated decision systems,” for 10 years. Industry experts warn that this potential regulatory vacuum would come precisely when AI systems are becoming more powerful and pervasive across the US society. Oversight gap raises concerns The moratorium would create an unprecedented situation: rapidly evolving AI technology would operate without state-level guardrails during what may be its most transformative decade. “The proposed decade-long moratorium on state-level AI regulations presents a double-edged sword,” said Abhivyakti Sengar, practice director at Everest Group. “On one hand, it aims to prevent a fragmented regulatory environment that could stifle innovation, on the other hand, it risks creating a regulatory vacuum, leaving critical decisions about AI governance in the hands of private entities without sufficient oversight.” The proposed legislation includes specific exceptions. According to the bill text, states would still be allowed to enforce laws that have “the primary purpose and effect of which is to remove legal impediments to, or facilitate the deployment or operation of, an artificial intelligence model, artificial intelligence system, or automated decision system.” States could also enforce laws that streamline “licensing, permitting, routing, zoning, procurement, or reporting procedures” for AI systems. However, the bill explicitly prohibits states from imposing “any substantive design, performance, data-handling, documentation, civil liability, taxation, fee, or other requirement” specifically on AI unless such requirements are applied equally to non-AI systems with similar functions. This limitation would prevent states from creating AI-specific oversight frameworks that address the technology’s unique capabilities and risks. State AI regulations threatened If enacted, the impact could be significant. Several states have been developing AI oversight frameworks that would likely become unenforceable under the federal provision. Various state-level efforts to regulate AI systems — from algorithmic transparency requirements to data privacy protections for AI training — could be effectively neutralized without public debate or input. The moratorium particularly threatens state data privacy protections. Without these state laws, consumers have few guarantees regarding how AI systems use their data, obtain consent, or make decisions affecting their lives. Global standards diverge The US approach now stands in stark contrast to the European Union’s comprehensive AI Act, which imposes strict requirements on high-risk AI systems. “As the US diverges from the EU’s stringent AI regulatory framework, multinational enterprises may face the challenge of navigating conflicting standards,” Sengar noted. This divergence potentially leads to “increased compliance costs and operational complexities.” Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research, sees a splintering global AI landscape ahead. “America’s moratorium will likely deepen the regulatory divergence with Europe,” said Gogia. “This will accelerate the fragmentation of global AI product design, where use-case eligibility and ethical thresholds vary dramatically by geography.” Enterprises face a new reality For businesses, the regulatory clarity comes with difficult strategic decisions. Companies must determine how aggressively to implement AI systems during this regulation-free decade. Many large companies aren’t waiting for government guidance. “Even before public oversight being put on hold, large enterprises have already launched internal AI governance councils,” Gogia explained. “These internal regimes — led by CISOs, legal, and risk teams — are becoming the primary referees for responsible AI use.” But Gogia cautioned against over-reliance on self-regulation: “While these structures are necessary, they are not a long-term substitute for statutory accountability.” Legal uncertainty remains Despite the moratorium on regulations, experts warn that companies still face significant liability risks. “The absence of clear legal guidelines could result in heightened legal uncertainty, as courts grapple with AI-related disputes without established precedents,” said Sengar. Gogia puts it more bluntly: “Even in a regulatory freeze, enterprises remain legally accountable. I believe the lack of specific laws does not eliminate legal exposure — it merely shifts the battleground from compliance desks to courtrooms.” While restricting state action, the legislation simultaneously expands the federal government’s AI footprint. The bill allocates $500 million to the Department of Commerce for AI modernization through 2035. The money targets legacy system replacement, operational efficiency improvements, and cybersecurity enhancements using AI technologies. This dual approach positions the federal government as both the primary AI regulator and a major AI customer, consolidating tremendous influence over the technology’s direction. Finding balance Industry observers emphasize the need for thoughtful governance despite the moratorium. “In this rapidly evolving landscape, a balanced approach that fosters innovation while ensuring accountability and public trust is paramount,” Sengar noted. Gogia offers a succinct assessment of the situation: “The 10-year moratorium on US state and local AI regulation removes complexity but not risk. I believe innovation does need room, but room without direction risks misalignment between corporate ethics and public interest.”

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80 Level shared a link

2025-05-14 12:10:00 ·

Mesh Match: Advanced Mesh Alignment & Retargeting In Maya
Back in March, we showcased Mesh Match, David Liebard's advanced mesh registration tool for Maya.
Since then, the developer has made significant progress, launching a dedicated website, releasing full documentation, and announcing that the Mesh Match for Maya 2023, 2024, and 2025 on both Windows and Linux will be publicly available before the end of this month.In case you missed it, Mesh Match brings mesh registration capabilities to Autodesk Maya, a feature that has never been natively available.
It enables precise alignment of meshes that share similar overall structures, even when their topologies differ in vertex count or order.
Mesh Match can also retarget deformations and transform matrices between models, whether or not they have identical topologies, while accurately considering local curvature and scale variations.Mesh Match is powered by a high-performance C++ deformer plug-in, with a Qt-based user interface that gives users intuitive access to its features and manages the overall workflow.
For those looking to integrate it into custom pipelines, an API is also available to automate tasks.To help users get started, Mesh Match offers in-depth documentation, tutorial videos, example scenes, and access to a dedicated Discord server for community support and discussion.
You can also try the beta for free through the Discord.Check out Mesh Match site here and join our 80 Level Talent platform and our new Discord server, follow us on Instagram, Twitter, LinkedIn, Telegram, TikTok, and Threads, where we share breakdowns, the latest news, awesome artworks, and more.
Source: https://80.lv/articles/mesh-match-advanced-mesh-alignment-retargeting-in-maya/" style="color: #0066cc;">https://80.lv/articles/mesh-match-advanced-mesh-alignment-retargeting-in-maya/
#mesh #match #advanced #alignment #ampamp #retargeting #maya

Mesh Match: Advanced Mesh Alignment & Retargeting In Maya
Back in March, we showcased Mesh Match, David Liebard's advanced mesh registration tool for Maya. Since then, the developer has made significant progress, launching a dedicated website, releasing full documentation, and announcing that the Mesh Match for Maya 2023, 2024, and 2025 on both Windows and Linux will be publicly available before the end of this month.In case you missed it, Mesh Match brings mesh registration capabilities to Autodesk Maya, a feature that has never been natively available. It enables precise alignment of meshes that share similar overall structures, even when their topologies differ in vertex count or order. Mesh Match can also retarget deformations and transform matrices between models, whether or not they have identical topologies, while accurately considering local curvature and scale variations.Mesh Match is powered by a high-performance C++ deformer plug-in, with a Qt-based user interface that gives users intuitive access to its features and manages the overall workflow. For those looking to integrate it into custom pipelines, an API is also available to automate tasks.To help users get started, Mesh Match offers in-depth documentation, tutorial videos, example scenes, and access to a dedicated Discord server for community support and discussion. You can also try the beta for free through the Discord.Check out Mesh Match site here and join our 80 Level Talent platform and our new Discord server, follow us on Instagram, Twitter, LinkedIn, Telegram, TikTok, and Threads, where we share breakdowns, the latest news, awesome artworks, and more. Source: https://80.lv/articles/mesh-match-advanced-mesh-alignment-retargeting-in-maya/ #mesh #match #advanced #alignment #ampamp #retargeting #maya

80.LV

Mesh Match: Advanced Mesh Alignment & Retargeting In Maya

Back in March, we showcased Mesh Match, David Liebard's advanced mesh registration tool for Maya. Since then, the developer has made significant progress, launching a dedicated website, releasing full documentation, and announcing that the Mesh Match for Maya 2023, 2024, and 2025 on both Windows and Linux will be publicly available before the end of this month.In case you missed it, Mesh Match brings mesh registration capabilities to Autodesk Maya, a feature that has never been natively available. It enables precise alignment of meshes that share similar overall structures, even when their topologies differ in vertex count or order. Mesh Match can also retarget deformations and transform matrices between models, whether or not they have identical topologies, while accurately considering local curvature and scale variations.Mesh Match is powered by a high-performance C++ deformer plug-in, with a Qt-based user interface that gives users intuitive access to its features and manages the overall workflow. For those looking to integrate it into custom pipelines, an API is also available to automate tasks.To help users get started, Mesh Match offers in-depth documentation, tutorial videos, example scenes, and access to a dedicated Discord server for community support and discussion. You can also try the beta for free through the Discord.Check out Mesh Match site here and join our 80 Level Talent platform and our new Discord server, follow us on Instagram, Twitter, LinkedIn, Telegram, TikTok, and Threads, where we share breakdowns, the latest news, awesome artworks, and more.

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Marktechpost AI shared a link

2025-05-14 10:42:56 ·

Agent-Based Debugging Gets a Cost-Effective Alternative: Salesforce AI Presents SWERank for Accurate and Scalable Software Issue Localization

Identifying the exact location of a software issue—such as a bug or feature request—remains one of the most labor-intensive tasks in the development lifecycle.
Despite advances in automated patch generation and code assistants, the process of pinpointing where in the codebase a change is needed often consumes more time than determining how to fix it.
Agent-based approaches powered by large language models (LLMs) have made headway by simulating developer workflows through iterative tool use and reasoning.
However, these systems are typically slow, brittle, and expensive to operate, especially when built on closed-source models.
In parallel, existing code retrieval models—while faster—are not optimized for the verbosity and behavioral focus of real-world issue descriptions.
This misalignment between natural language inputs and code search capability presents a fundamental challenge for scalable automated debugging.
SWERank — A Practical Framework for Precise Localization
To address these limitations, Salesforce AI has introduced SWERank, a lightweight and effective retrieve-and-rerank framework tailored for software issue localization.
SWERank is designed to bridge the gap between efficiency and precision by reframing localization as a code ranking task.
The framework consists of two key components:
SWERankEmbed, a bi-encoder retrieval model that encodes GitHub issues and code snippets into a shared embedding space for efficient similarity-based retrieval.
SWERankLLM, a listwise reranker built on instruction-tuned LLMs that refines the ranking of retrieved candidates using contextual understanding.
To train this system, the research team curated SWELOC, a large-scale dataset extracted from public GitHub repositories, linking real-world issue reports with corresponding code changes.
SWELOC introduces contrastive training examples using consistency filtering and hard-negative mining to ensure data quality and relevance.
Architecture and Methodological Contributions
At its core, SWERank follows a two-stage pipeline.
First, SWERankEmbed maps a given issue description and candidate functions into dense vector representations.
Using a contrastive InfoNCE loss, the retriever is trained to increase the similarity between an issue and its true associated function while reducing its similarity to unrelated code snippets.
Notably, the model benefits from carefully mined hard negatives—code functions that are semantically similar but not relevant—which improve the model’s discriminative capability.
The reranking stage leverages SWERankLLM, a listwise LLM-based reranker that processes an issue description along with top-k code candidates and generates a ranked list where the relevant code appears at the top.
Importantly, the training objective is adapted to settings where only the true positive is known.
The model is trained to output the identifier of the relevant code snippet, maintaining compatibility with listwise inference while simplifying the supervision process.
Together, these components allow SWERank to offer high performance without requiring multiple rounds of interaction or costly agent orchestration.
Insights
Evaluations on SWE-Bench-Lite and LocBench—two standard benchmarks for software localization—demonstrate that SWERank achieves state-of-the-art results across file, module, and function levels.
On SWE-Bench-Lite, SWERankEmbed-Large (7B) attained a function-level accuracy@10 of 82.12%, outperforming even LocAgent running with Claude-3.5.
When coupled with SWERankLLM-Large (32B), performance further improved to 88.69%, establishing a new benchmark for this task.
In addition to performance gains, SWERank offers substantial cost benefits.
Compared to Claude-powered agents, which average around $0.66 per example, SWERankLLM’s inference cost is $0.011 for the 7B model and $0.015 for the 32B variant—delivering up to 6x better accuracy-to-cost ratio.
Moreover, the 137M parameter SWERankEmbed-Small model achieves competitive results, demonstrating the framework’s scalability and efficiency even on lightweight architectures.
Beyond benchmark performance, experiments also show that SWELOC data improves a broad class of embedding and reranking models.
Models pre-trained for general-purpose retrieval exhibited significant accuracy gains when fine-tuned with SWELOC, validating its utility as a training resource for issue localization tasks.
Conclusion
SWERank introduces a compelling alternative to traditional agent-based localization approaches by modeling software issue localization as a ranking problem.
Through its retrieve-and-rerank architecture, SWERank delivers state-of-the-art accuracy while maintaining low inference cost and minimal latency.
The accompanying SWELOC dataset provides a high-quality training foundation, enabling robust generalization across various codebases and issue types.
By decoupling localization from agentic multi-step reasoning and grounding it in efficient neural retrieval, Salesforce AI demonstrates that practical, scalable solutions for debugging and code maintenance are not only possible—but well within reach using open-source tools.
SWERank sets a new bar for accuracy, efficiency, and deployability in automated software engineering.
Check out the Paper and Project Page. All credit for this research goes to the researchers of this project.
Also, feel free to follow us on Twitter and don’t forget to join our 90k+ ML SubReddit.
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Asif RazzaqWebsite | + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc..
As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good.
His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience.
The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/A" style="color: #0066cc;">https://www.marktechpost.com/author/6flvq/A Step-by-Step Guide to Build a Fast Semantic Search and RAG QA Engine on Web-Scraped Data Using Together AI Embeddings, FAISS Retrieval, and LangChainAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A" style="color: #0066cc;">https://www.marktechpost.com/author/6flvq/A Step-by-Step Guide to Deploy a Fully Integrated Firecrawl-Powered MCP Server on Claude Desktop with Smithery and VeryaXAsif Razzaqhttps://www.marktechpost.com/author/6flvq/OpenAI" style="color: #0066cc;">https://www.marktechpost.com/author/6flvq/OpenAI Releases HealthBench: An Open-Source Benchmark for Measuring the Performance and Safety of Large Language Models in HealthcareAsif Razzaqhttps://www.marktechpost.com/author/6flvq/PrimeIntellect" style="color: #0066cc;">https://www.marktechpost.com/author/6flvq/PrimeIntellect Releases INTELLECT-2: A 32B Reasoning Model Trained via Distributed Asynchronous Reinforcement Learning

Source: https://www.marktechpost.com/2025/05/13/agent-based-debugging-gets-a-cost-effective-alternative-salesforce-ai-presents-swerank-for-accurate-and-scalable-software-issue-localization/">https://www.marktechpost.com/2025/05/13/agent-based-debugging-gets-a-cost-effective-alternative-salesforce-ai-presents-swerank-for-accurate-and-scalable-software-issue-localization/">https://www.marktechpost.com/2025/05/13/agent-based-debugging-gets-a-cost-effective-alternative-salesforce-ai-presents-swerank-for-accurate-and-scalable-software-issue-localization/
#agentbased #debugging #gets #costeffective #alternative #salesforce #presents #swerank #for #accurate #and #scalable #software #issue #localization

Agent-Based Debugging Gets a Cost-Effective Alternative: Salesforce AI Presents SWERank for Accurate and Scalable Software Issue Localization
Identifying the exact location of a software issue—such as a bug or feature request—remains one of the most labor-intensive tasks in the development lifecycle. Despite advances in automated patch generation and code assistants, the process of pinpointing where in the codebase a change is needed often consumes more time than determining how to fix it. Agent-based approaches powered by large language models (LLMs) have made headway by simulating developer workflows through iterative tool use and reasoning. However, these systems are typically slow, brittle, and expensive to operate, especially when built on closed-source models. In parallel, existing code retrieval models—while faster—are not optimized for the verbosity and behavioral focus of real-world issue descriptions. This misalignment between natural language inputs and code search capability presents a fundamental challenge for scalable automated debugging. SWERank — A Practical Framework for Precise Localization To address these limitations, Salesforce AI has introduced SWERank, a lightweight and effective retrieve-and-rerank framework tailored for software issue localization. SWERank is designed to bridge the gap between efficiency and precision by reframing localization as a code ranking task. The framework consists of two key components: SWERankEmbed, a bi-encoder retrieval model that encodes GitHub issues and code snippets into a shared embedding space for efficient similarity-based retrieval. SWERankLLM, a listwise reranker built on instruction-tuned LLMs that refines the ranking of retrieved candidates using contextual understanding. To train this system, the research team curated SWELOC, a large-scale dataset extracted from public GitHub repositories, linking real-world issue reports with corresponding code changes. SWELOC introduces contrastive training examples using consistency filtering and hard-negative mining to ensure data quality and relevance. Architecture and Methodological Contributions At its core, SWERank follows a two-stage pipeline. First, SWERankEmbed maps a given issue description and candidate functions into dense vector representations. Using a contrastive InfoNCE loss, the retriever is trained to increase the similarity between an issue and its true associated function while reducing its similarity to unrelated code snippets. Notably, the model benefits from carefully mined hard negatives—code functions that are semantically similar but not relevant—which improve the model’s discriminative capability. The reranking stage leverages SWERankLLM, a listwise LLM-based reranker that processes an issue description along with top-k code candidates and generates a ranked list where the relevant code appears at the top. Importantly, the training objective is adapted to settings where only the true positive is known. The model is trained to output the identifier of the relevant code snippet, maintaining compatibility with listwise inference while simplifying the supervision process. Together, these components allow SWERank to offer high performance without requiring multiple rounds of interaction or costly agent orchestration. Insights Evaluations on SWE-Bench-Lite and LocBench—two standard benchmarks for software localization—demonstrate that SWERank achieves state-of-the-art results across file, module, and function levels. On SWE-Bench-Lite, SWERankEmbed-Large (7B) attained a function-level accuracy@10 of 82.12%, outperforming even LocAgent running with Claude-3.5. When coupled with SWERankLLM-Large (32B), performance further improved to 88.69%, establishing a new benchmark for this task. In addition to performance gains, SWERank offers substantial cost benefits. Compared to Claude-powered agents, which average around $0.66 per example, SWERankLLM’s inference cost is $0.011 for the 7B model and $0.015 for the 32B variant—delivering up to 6x better accuracy-to-cost ratio. Moreover, the 137M parameter SWERankEmbed-Small model achieves competitive results, demonstrating the framework’s scalability and efficiency even on lightweight architectures. Beyond benchmark performance, experiments also show that SWELOC data improves a broad class of embedding and reranking models. Models pre-trained for general-purpose retrieval exhibited significant accuracy gains when fine-tuned with SWELOC, validating its utility as a training resource for issue localization tasks. Conclusion SWERank introduces a compelling alternative to traditional agent-based localization approaches by modeling software issue localization as a ranking problem. Through its retrieve-and-rerank architecture, SWERank delivers state-of-the-art accuracy while maintaining low inference cost and minimal latency. The accompanying SWELOC dataset provides a high-quality training foundation, enabling robust generalization across various codebases and issue types. By decoupling localization from agentic multi-step reasoning and grounding it in efficient neural retrieval, Salesforce AI demonstrates that practical, scalable solutions for debugging and code maintenance are not only possible—but well within reach using open-source tools. SWERank sets a new bar for accuracy, efficiency, and deployability in automated software engineering. Check out the Paper and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 90k+ ML SubReddit. Here’s a brief overview of what we’re building at Marktechpost: ML News Community – r/machinelearningnews (92k+ members) Newsletter– airesearchinsights.com/(30k+ subscribers) miniCON AI Events – minicon.marktechpost.com AI Reports & Magazines – magazine.marktechpost.com AI Dev & Research News – marktechpost.com (1M+ monthly readers) Partner with us Asif RazzaqWebsite | + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Guide to Build a Fast Semantic Search and RAG QA Engine on Web-Scraped Data Using Together AI Embeddings, FAISS Retrieval, and LangChainAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Guide to Deploy a Fully Integrated Firecrawl-Powered MCP Server on Claude Desktop with Smithery and VeryaXAsif Razzaqhttps://www.marktechpost.com/author/6flvq/OpenAI Releases HealthBench: An Open-Source Benchmark for Measuring the Performance and Safety of Large Language Models in HealthcareAsif Razzaqhttps://www.marktechpost.com/author/6flvq/PrimeIntellect Releases INTELLECT-2: A 32B Reasoning Model Trained via Distributed Asynchronous Reinforcement Learning Source: https://www.marktechpost.com/2025/05/13/agent-based-debugging-gets-a-cost-effective-alternative-salesforce-ai-presents-swerank-for-accurate-and-scalable-software-issue-localization/ #agentbased #debugging #gets #costeffective #alternative #salesforce #presents #swerank #for #accurate #and #scalable #software #issue #localization

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Agent-Based Debugging Gets a Cost-Effective Alternative: Salesforce AI Presents SWERank for Accurate and Scalable Software Issue Localization

Identifying the exact location of a software issue—such as a bug or feature request—remains one of the most labor-intensive tasks in the development lifecycle. Despite advances in automated patch generation and code assistants, the process of pinpointing where in the codebase a change is needed often consumes more time than determining how to fix it. Agent-based approaches powered by large language models (LLMs) have made headway by simulating developer workflows through iterative tool use and reasoning. However, these systems are typically slow, brittle, and expensive to operate, especially when built on closed-source models. In parallel, existing code retrieval models—while faster—are not optimized for the verbosity and behavioral focus of real-world issue descriptions. This misalignment between natural language inputs and code search capability presents a fundamental challenge for scalable automated debugging. SWERank — A Practical Framework for Precise Localization To address these limitations, Salesforce AI has introduced SWERank, a lightweight and effective retrieve-and-rerank framework tailored for software issue localization. SWERank is designed to bridge the gap between efficiency and precision by reframing localization as a code ranking task. The framework consists of two key components: SWERankEmbed, a bi-encoder retrieval model that encodes GitHub issues and code snippets into a shared embedding space for efficient similarity-based retrieval. SWERankLLM, a listwise reranker built on instruction-tuned LLMs that refines the ranking of retrieved candidates using contextual understanding. To train this system, the research team curated SWELOC, a large-scale dataset extracted from public GitHub repositories, linking real-world issue reports with corresponding code changes. SWELOC introduces contrastive training examples using consistency filtering and hard-negative mining to ensure data quality and relevance. Architecture and Methodological Contributions At its core, SWERank follows a two-stage pipeline. First, SWERankEmbed maps a given issue description and candidate functions into dense vector representations. Using a contrastive InfoNCE loss, the retriever is trained to increase the similarity between an issue and its true associated function while reducing its similarity to unrelated code snippets. Notably, the model benefits from carefully mined hard negatives—code functions that are semantically similar but not relevant—which improve the model’s discriminative capability. The reranking stage leverages SWERankLLM, a listwise LLM-based reranker that processes an issue description along with top-k code candidates and generates a ranked list where the relevant code appears at the top. Importantly, the training objective is adapted to settings where only the true positive is known. The model is trained to output the identifier of the relevant code snippet, maintaining compatibility with listwise inference while simplifying the supervision process. Together, these components allow SWERank to offer high performance without requiring multiple rounds of interaction or costly agent orchestration. Insights Evaluations on SWE-Bench-Lite and LocBench—two standard benchmarks for software localization—demonstrate that SWERank achieves state-of-the-art results across file, module, and function levels. On SWE-Bench-Lite, SWERankEmbed-Large (7B) attained a function-level accuracy@10 of 82.12%, outperforming even LocAgent running with Claude-3.5. When coupled with SWERankLLM-Large (32B), performance further improved to 88.69%, establishing a new benchmark for this task. In addition to performance gains, SWERank offers substantial cost benefits. Compared to Claude-powered agents, which average around $0.66 per example, SWERankLLM’s inference cost is $0.011 for the 7B model and $0.015 for the 32B variant—delivering up to 6x better accuracy-to-cost ratio. Moreover, the 137M parameter SWERankEmbed-Small model achieves competitive results, demonstrating the framework’s scalability and efficiency even on lightweight architectures. Beyond benchmark performance, experiments also show that SWELOC data improves a broad class of embedding and reranking models. Models pre-trained for general-purpose retrieval exhibited significant accuracy gains when fine-tuned with SWELOC, validating its utility as a training resource for issue localization tasks. Conclusion SWERank introduces a compelling alternative to traditional agent-based localization approaches by modeling software issue localization as a ranking problem. Through its retrieve-and-rerank architecture, SWERank delivers state-of-the-art accuracy while maintaining low inference cost and minimal latency. The accompanying SWELOC dataset provides a high-quality training foundation, enabling robust generalization across various codebases and issue types. By decoupling localization from agentic multi-step reasoning and grounding it in efficient neural retrieval, Salesforce AI demonstrates that practical, scalable solutions for debugging and code maintenance are not only possible—but well within reach using open-source tools. SWERank sets a new bar for accuracy, efficiency, and deployability in automated software engineering. Check out the Paper and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 90k+ ML SubReddit. Here’s a brief overview of what we’re building at Marktechpost: ML News Community – r/machinelearningnews (92k+ members) Newsletter– airesearchinsights.com/(30k+ subscribers) miniCON AI Events – minicon.marktechpost.com AI Reports & Magazines – magazine.marktechpost.com AI Dev & Research News – marktechpost.com (1M+ monthly readers) Partner with us Asif RazzaqWebsite | + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Guide to Build a Fast Semantic Search and RAG QA Engine on Web-Scraped Data Using Together AI Embeddings, FAISS Retrieval, and LangChainAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Guide to Deploy a Fully Integrated Firecrawl-Powered MCP Server on Claude Desktop with Smithery and VeryaXAsif Razzaqhttps://www.marktechpost.com/author/6flvq/OpenAI Releases HealthBench: An Open-Source Benchmark for Measuring the Performance and Safety of Large Language Models in HealthcareAsif Razzaqhttps://www.marktechpost.com/author/6flvq/PrimeIntellect Releases INTELLECT-2: A 32B Reasoning Model Trained via Distributed Asynchronous Reinforcement Learning

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2025-05-14 10:42:47 ·

$TRUMP meme coin buyers spent more than $140 million to get dinner invite with Trump, data shows

MoneyWatch
$TRUMP meme coin buyers spent more than $140 million to get dinner invite with Trump, data shows
How Trump's made money off his meme coin
Buyers spent more than $140 million to snap up $TRUMP meme coins in a bid to secure an invitation to an "intimate dinner" with President Trump, according to data analytics firm Inca Digital. On April 23, the digital currency got a boost when it announced that its top 220 holders as of May 12 would be invited to a dinner with the president.
That helped raise the value of the meme coin by more than 40% from the announcement through May 12, partially reversing an 88% slump in the weeks prior.The top holders of the $TRUMP meme coin spent anywhere from as little as $53,500 to as much as $16.4 million to grab one of the dinner invites with Mr.
Trump, Inca Digital's analysis found. As of the contest's end on May 12, the biggest holder of the digital currency was an account named Sun VIP, according to the analysis.
While it's impossible to know the identity of any of the account owners due to the anonymity of digital wallets, many of the purchases were made through exchanges that don't accept U.S.
customers, such as Bybit and Gate.io, Inca's research reveals. "A ton of these users sent funds to international exchanges," noted Austin Ryan, director of marketing for Inca Digital, a firm that analyzes crypto data for risk issues like liquidity and front-running.
"The thought is that if they are interacting with those exchanges, they are not in the U.S."
The dinner contest has raised concerns from government watchdogs and Democratic lawmakers about purchases of a Trump-branded asset being linked to access to the president.
Also, because the $TRUMP coin is partly owned by an affiliate of the Trump Organization, the president's real estate organization, he and his family stand to benefit financially from purchases of the meme coin, critics add.The @TRUMP meme coin site hasn't disclosed the identities of the 220 top holders, who under the promotion are in line to dine with Mr.
Trump.
Tony Carrk, the executive director of watchdog group Accountable.US, expressed concern because those holders are "shrouded by their anonymous crypto usernames." Neither the White House nor the Trump Organization immediately responded to requests for comment.The dinner will take place on May 22 at the Trump National Golf Club in Washington, D.C., according to the $TRUMP meme coin website.
The top 25 holders will "will enjoy a Private VIP Reception with President TRUMP" as well as a "Special VIP TOUR," the site adds.
Eric Trump, one of Mr.
Trump's sons, in January called the $TRUMP coin "the hottest digital meme on earth."How Trump makes money from his meme coinDecentralization is foundational to cryptocurrency.
Bitcoin, the world's most popular crypto, was born in the wake of the 2008 financial crisis as a digital currency meant to be uncontrolled by banks or governments.$TRUMP meme coins can be traded on a decentralized exchange, which is essentially a place where traders can swap goods without a middleman.Instead of matching buyers and sellers one by one, decentralized exchanges use something called a liquidity pool to ensure trades can happen easily and instantly.
Liquidity pools are essentially an automated pot of funds that pair meme coins like $TRUMP with more popular types of crypto that can be easily traded.When the $TRUMP meme coin was first launched, its creators initially released 20% of the planned 1 billion total coins.
Half of that 20% was put up for public sale while the other half was put into a liquidity pool.
CIC Digital, an affiliate of the Trump Organization, and another company called Fight Fight Fight, receive "trading revenue derived from trading activities" of the Trump meme coins, according to its website.Through the liquidity pool, the creators of Mr.
Trump's meme coins make money by charging tiny transactional fees on each trade.
More from CBS News
Aimee Picchi
Aimee Picchi is the associate managing editor for CBS MoneyWatch, where she covers business and personal finance.
She previously worked at Bloomberg News and has written for national news outlets including USA Today and Consumer Reports.
The Associated Press

contributed to this report.

Source: https://www.cbsnews.com/news/trump-meme-coin-top-220-buyers-spent-140-million-analysis/" style="color: #0066cc;">https://www.cbsnews.com/news/trump-meme-coin-top-220-buyers-spent-140-million-analysis/
#trump #meme #coin #buyers #spent #more #than #million #get #dinner #invite #with #data #shows

$TRUMP meme coin buyers spent more than $140 million to get dinner invite with Trump, data shows
MoneyWatch $TRUMP meme coin buyers spent more than $140 million to get dinner invite with Trump, data shows How Trump's made money off his meme coin Buyers spent more than $140 million to snap up $TRUMP meme coins in a bid to secure an invitation to an "intimate dinner" with President Trump, according to data analytics firm Inca Digital. On April 23, the digital currency got a boost when it announced that its top 220 holders as of May 12 would be invited to a dinner with the president. That helped raise the value of the meme coin by more than 40% from the announcement through May 12, partially reversing an 88% slump in the weeks prior.The top holders of the $TRUMP meme coin spent anywhere from as little as $53,500 to as much as $16.4 million to grab one of the dinner invites with Mr. Trump, Inca Digital's analysis found. As of the contest's end on May 12, the biggest holder of the digital currency was an account named Sun VIP, according to the analysis. While it's impossible to know the identity of any of the account owners due to the anonymity of digital wallets, many of the purchases were made through exchanges that don't accept U.S. customers, such as Bybit and Gate.io, Inca's research reveals. "A ton of these users sent funds to international exchanges," noted Austin Ryan, director of marketing for Inca Digital, a firm that analyzes crypto data for risk issues like liquidity and front-running. "The thought is that if they are interacting with those exchanges, they are not in the U.S." The dinner contest has raised concerns from government watchdogs and Democratic lawmakers about purchases of a Trump-branded asset being linked to access to the president. Also, because the $TRUMP coin is partly owned by an affiliate of the Trump Organization, the president's real estate organization, he and his family stand to benefit financially from purchases of the meme coin, critics add.The @TRUMP meme coin site hasn't disclosed the identities of the 220 top holders, who under the promotion are in line to dine with Mr. Trump. Tony Carrk, the executive director of watchdog group Accountable.US, expressed concern because those holders are "shrouded by their anonymous crypto usernames." Neither the White House nor the Trump Organization immediately responded to requests for comment.The dinner will take place on May 22 at the Trump National Golf Club in Washington, D.C., according to the $TRUMP meme coin website. The top 25 holders will "will enjoy a Private VIP Reception with President TRUMP" as well as a "Special VIP TOUR," the site adds. Eric Trump, one of Mr. Trump's sons, in January called the $TRUMP coin "the hottest digital meme on earth."How Trump makes money from his meme coinDecentralization is foundational to cryptocurrency. Bitcoin, the world's most popular crypto, was born in the wake of the 2008 financial crisis as a digital currency meant to be uncontrolled by banks or governments.$TRUMP meme coins can be traded on a decentralized exchange, which is essentially a place where traders can swap goods without a middleman.Instead of matching buyers and sellers one by one, decentralized exchanges use something called a liquidity pool to ensure trades can happen easily and instantly. Liquidity pools are essentially an automated pot of funds that pair meme coins like $TRUMP with more popular types of crypto that can be easily traded.When the $TRUMP meme coin was first launched, its creators initially released 20% of the planned 1 billion total coins. Half of that 20% was put up for public sale while the other half was put into a liquidity pool. CIC Digital, an affiliate of the Trump Organization, and another company called Fight Fight Fight, receive "trading revenue derived from trading activities" of the Trump meme coins, according to its website.Through the liquidity pool, the creators of Mr. Trump's meme coins make money by charging tiny transactional fees on each trade. More from CBS News Aimee Picchi Aimee Picchi is the associate managing editor for CBS MoneyWatch, where she covers business and personal finance. She previously worked at Bloomberg News and has written for national news outlets including USA Today and Consumer Reports. The Associated Press contributed to this report. Source: https://www.cbsnews.com/news/trump-meme-coin-top-220-buyers-spent-140-million-analysis/ #trump #meme #coin #buyers #spent #more #than #million #get #dinner #invite #with #data #shows

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$TRUMP meme coin buyers spent more than $140 million to get dinner invite with Trump, data shows

MoneyWatch $TRUMP meme coin buyers spent more than $140 million to get dinner invite with Trump, data shows How Trump's made money off his meme coin Buyers spent more than $140 million to snap up $TRUMP meme coins in a bid to secure an invitation to an "intimate dinner" with President Trump, according to data analytics firm Inca Digital. On April 23, the digital currency got a boost when it announced that its top 220 holders as of May 12 would be invited to a dinner with the president. That helped raise the value of the meme coin by more than 40% from the announcement through May 12, partially reversing an 88% slump in the weeks prior.The top holders of the $TRUMP meme coin spent anywhere from as little as $53,500 to as much as $16.4 million to grab one of the dinner invites with Mr. Trump, Inca Digital's analysis found. As of the contest's end on May 12, the biggest holder of the digital currency was an account named Sun VIP, according to the analysis. While it's impossible to know the identity of any of the account owners due to the anonymity of digital wallets, many of the purchases were made through exchanges that don't accept U.S. customers, such as Bybit and Gate.io, Inca's research reveals. "A ton of these users sent funds to international exchanges," noted Austin Ryan, director of marketing for Inca Digital, a firm that analyzes crypto data for risk issues like liquidity and front-running. "The thought is that if they are interacting with those exchanges, they are not in the U.S." The dinner contest has raised concerns from government watchdogs and Democratic lawmakers about purchases of a Trump-branded asset being linked to access to the president. Also, because the $TRUMP coin is partly owned by an affiliate of the Trump Organization, the president's real estate organization, he and his family stand to benefit financially from purchases of the meme coin, critics add.The @TRUMP meme coin site hasn't disclosed the identities of the 220 top holders, who under the promotion are in line to dine with Mr. Trump. Tony Carrk, the executive director of watchdog group Accountable.US, expressed concern because those holders are "shrouded by their anonymous crypto usernames." Neither the White House nor the Trump Organization immediately responded to requests for comment.The dinner will take place on May 22 at the Trump National Golf Club in Washington, D.C., according to the $TRUMP meme coin website. The top 25 holders will "will enjoy a Private VIP Reception with President TRUMP" as well as a "Special VIP TOUR," the site adds. Eric Trump, one of Mr. Trump's sons, in January called the $TRUMP coin "the hottest digital meme on earth."How Trump makes money from his meme coinDecentralization is foundational to cryptocurrency. Bitcoin, the world's most popular crypto, was born in the wake of the 2008 financial crisis as a digital currency meant to be uncontrolled by banks or governments.$TRUMP meme coins can be traded on a decentralized exchange, which is essentially a place where traders can swap goods without a middleman.Instead of matching buyers and sellers one by one, decentralized exchanges use something called a liquidity pool to ensure trades can happen easily and instantly. Liquidity pools are essentially an automated pot of funds that pair meme coins like $TRUMP with more popular types of crypto that can be easily traded.When the $TRUMP meme coin was first launched, its creators initially released 20% of the planned 1 billion total coins. Half of that 20% was put up for public sale while the other half was put into a liquidity pool. CIC Digital, an affiliate of the Trump Organization, and another company called Fight Fight Fight, receive "trading revenue derived from trading activities" of the Trump meme coins, according to its website.Through the liquidity pool, the creators of Mr. Trump's meme coins make money by charging tiny transactional fees on each trade. More from CBS News Aimee Picchi Aimee Picchi is the associate managing editor for CBS MoneyWatch, where she covers business and personal finance. She previously worked at Bloomberg News and has written for national news outlets including USA Today and Consumer Reports. The Associated Press contributed to this report.

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2025-05-14 10:38:40 ·

New Law Would Ban All AI Regulation for a Decade
This is insane.Fresh HellRepublican lawmakers slipped language into the Budget Reconciliation Bill this week that would ban AI regulation, on the federal and state levels, for a decade, as 404 Media reports.An updated version of the bill introduced last night by Congressman Brett Guthrie (R-KY), who chairs the House Committee on Energy and Commerce, includes a new and sweeping clause about AI advancement declaring that "no State or political subdivision thereof may enforce any law or regulation regulating artificial intelligence models, artificial intelligence systems, or automated decision systems during the ten year period beginning on the date of the enactment of this Act."It's a remarkably expansive provision that, as 404 notes, likely reflects the engraining of Silicon Valley figures and influences into Washington and the White House.
Tech CEOs have vied for president Donald Trump's attention since he was inaugurated, and the American tech industry writ large has become a fierce and powerful lobbying force.
The Trump administration is also stacked with AI-invested tech moguls like David Sacks, Marc Andreessen, and Elon Musk.Meanwhile, the impacts of a regulation-free AI landscape are already being felt.
Emotive, addictive AI companions have been rolled out explicitly to teenagers without evidence of safety, AI companies are missing their climate targets and spewing unchecked emissions into American neighborhoods, and nonconsensual deepfakes of women and girls are flooding social media.No regulation will likely mean a lot more fresh hell where that came from — and little chance of stemming the tide.Blank ChecksThe update in the proposed law also seeks to appropriate a staggering $500 million over ten years to fund efforts to infuse the federal government's IT systems with "commercial" AI tech and unnamed "automation technologies."In other words, not only does the government want to completely stifle efforts to regulate a fast-developing technology, it also wants to integrate those unregulated technologies into the beating digital heart of the federal government.The bill also comes after states including New York and California have worked to pass some limited AI regulations, as 404 notes.
Were the bill to be signed into law, it would seemingly render those laws — which, for instance, ensure that employers review AI hiring tools for bias — unenforceable.As it stands, the bill is in limbo.
The proposal is massive, and includes drastic spending cuts to services like Medicaid and climate funds, slashes that Democrats largely oppose; Republican budget hawks, meanwhile, have raised concerns over the bill's hefty price tag.Whether it survives in its current form — its controversial AI provisions included — remains to be seen.More on AI and regulation: Signs Grow That AI Is Starting to Seriously Bite Into the Job MarketShare This Article
Source: https://futurism.com/the-byte/new-law-ban-ai-regulation" style="color: #0066cc;">https://futurism.com/the-byte/new-law-ban-ai-regulation
#new #law #would #ban #all #regulation #for #decade

New Law Would Ban All AI Regulation for a Decade
This is insane.Fresh HellRepublican lawmakers slipped language into the Budget Reconciliation Bill this week that would ban AI regulation, on the federal and state levels, for a decade, as 404 Media reports.An updated version of the bill introduced last night by Congressman Brett Guthrie (R-KY), who chairs the House Committee on Energy and Commerce, includes a new and sweeping clause about AI advancement declaring that "no State or political subdivision thereof may enforce any law or regulation regulating artificial intelligence models, artificial intelligence systems, or automated decision systems during the ten year period beginning on the date of the enactment of this Act."It's a remarkably expansive provision that, as 404 notes, likely reflects the engraining of Silicon Valley figures and influences into Washington and the White House. Tech CEOs have vied for president Donald Trump's attention since he was inaugurated, and the American tech industry writ large has become a fierce and powerful lobbying force. The Trump administration is also stacked with AI-invested tech moguls like David Sacks, Marc Andreessen, and Elon Musk.Meanwhile, the impacts of a regulation-free AI landscape are already being felt. Emotive, addictive AI companions have been rolled out explicitly to teenagers without evidence of safety, AI companies are missing their climate targets and spewing unchecked emissions into American neighborhoods, and nonconsensual deepfakes of women and girls are flooding social media.No regulation will likely mean a lot more fresh hell where that came from — and little chance of stemming the tide.Blank ChecksThe update in the proposed law also seeks to appropriate a staggering $500 million over ten years to fund efforts to infuse the federal government's IT systems with "commercial" AI tech and unnamed "automation technologies."In other words, not only does the government want to completely stifle efforts to regulate a fast-developing technology, it also wants to integrate those unregulated technologies into the beating digital heart of the federal government.The bill also comes after states including New York and California have worked to pass some limited AI regulations, as 404 notes. Were the bill to be signed into law, it would seemingly render those laws — which, for instance, ensure that employers review AI hiring tools for bias — unenforceable.As it stands, the bill is in limbo. The proposal is massive, and includes drastic spending cuts to services like Medicaid and climate funds, slashes that Democrats largely oppose; Republican budget hawks, meanwhile, have raised concerns over the bill's hefty price tag.Whether it survives in its current form — its controversial AI provisions included — remains to be seen.More on AI and regulation: Signs Grow That AI Is Starting to Seriously Bite Into the Job MarketShare This Article Source: https://futurism.com/the-byte/new-law-ban-ai-regulation #new #law #would #ban #all #regulation #for #decade

FUTURISM.COM

New Law Would Ban All AI Regulation for a Decade

This is insane.Fresh HellRepublican lawmakers slipped language into the Budget Reconciliation Bill this week that would ban AI regulation, on the federal and state levels, for a decade, as 404 Media reports.An updated version of the bill introduced last night by Congressman Brett Guthrie (R-KY), who chairs the House Committee on Energy and Commerce, includes a new and sweeping clause about AI advancement declaring that "no State or political subdivision thereof may enforce any law or regulation regulating artificial intelligence models, artificial intelligence systems, or automated decision systems during the ten year period beginning on the date of the enactment of this Act."It's a remarkably expansive provision that, as 404 notes, likely reflects the engraining of Silicon Valley figures and influences into Washington and the White House. Tech CEOs have vied for president Donald Trump's attention since he was inaugurated, and the American tech industry writ large has become a fierce and powerful lobbying force. The Trump administration is also stacked with AI-invested tech moguls like David Sacks, Marc Andreessen, and Elon Musk.Meanwhile, the impacts of a regulation-free AI landscape are already being felt. Emotive, addictive AI companions have been rolled out explicitly to teenagers without evidence of safety, AI companies are missing their climate targets and spewing unchecked emissions into American neighborhoods, and nonconsensual deepfakes of women and girls are flooding social media.No regulation will likely mean a lot more fresh hell where that came from — and little chance of stemming the tide.Blank ChecksThe update in the proposed law also seeks to appropriate a staggering $500 million over ten years to fund efforts to infuse the federal government's IT systems with "commercial" AI tech and unnamed "automation technologies."In other words, not only does the government want to completely stifle efforts to regulate a fast-developing technology, it also wants to integrate those unregulated technologies into the beating digital heart of the federal government.The bill also comes after states including New York and California have worked to pass some limited AI regulations, as 404 notes. Were the bill to be signed into law, it would seemingly render those laws — which, for instance, ensure that employers review AI hiring tools for bias — unenforceable.As it stands, the bill is in limbo. The proposal is massive, and includes drastic spending cuts to services like Medicaid and climate funds, slashes that Democrats largely oppose; Republican budget hawks, meanwhile, have raised concerns over the bill's hefty price tag.Whether it survives in its current form — its controversial AI provisions included — remains to be seen.More on AI and regulation: Signs Grow That AI Is Starting to Seriously Bite Into the Job MarketShare This Article

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2025-05-14 10:09:46 ·

Google I/O 2025: What to expect including Gemini AI, Android 16 updates, Android XR and more
In just a week, Google's annual developer conference will kick off on May 20.
The event is probably the most important on the company's calendar, offering a glimpse at everything it has been working on over the past year.
From the rumors and even information Google has trickled out, I/O 2025 should be one of the more exciting tech keynotes in recent memory.
Plus, for the first time, Google has spun out a dedicated Android showcase planned a whole week earlier.
That just happened today (May 13), and you can check out everything that was announced at the Android Show or go to our liveblog to get a feel for how things played out.
Now that the Android Show is over, it's time to look ahead to I/O, where the focus will almost definitely be about AI.
We've gathered the most credible reports and leaks to put together this roundup of what to expect, and though most of the Android-related announcements have been made, it's still possible that Google shares more details about its mobile platform next week.
If you'd like to tune in from home and follow along as Google makes its announcements, check out our article on how to watch the Google I/O 2025 keynote.
We'll also be liveblogging the event, so you can just come to Engadget for the breaking news.
Android 16
Some of my favorite I/O moments involved watching Dave Burke take to the Shoreline stage to talk about the latest updates for Android.
But for the past couple of years, Android hasn't had much of a spotlight at Google's annual developer conference.
That's about to change, with the company's dedicated showcase during today's Android Show: I/O Edition.
Get a front row seat to The Android Show: I/O Edition May 13, 10 AM PT.
Meet our team and learn about new experiences coming to Android.
Set a reminder and be the first in the know → https://t.co/z8QLNSYkl6" style="color: #0066cc;">https://t.co/z8QLNSYkl6 #TheAndroidShow pic.twitter.com/RTzTOwUtFN— Android (@Android) April 28, 2025

The presentation featured Android Ecosystem President Sameer Samat, who took over for Burke in 2024.
We saw Samat and his colleagues show off the new Material 3 Expressive design, and what we learned confirmed some of the features that were previously leaked, like the "Ongoing notifications" bar.
Material 3 Expressive is also coming to Wear OS 6, and the company is expanding the reach of Gemini by bringing it to its smartwatch platform, Android Auto and Google TV.
Android 16 will also come with new scam-detection features and a refined Find Hub that will see support for satellite connectivity later in the year.
Speaking of timing, Google has already confirmed the new operating system will arrive sometime before the second half of the year.
Though it did not release a stable build of Android 16 today, Samat shared during the show that Android 16 (or at least part of it) is coming next month to Pixel devices.
And though the company did cover some new features coming to Android XR, senior director for Android Product and UX Guemmy Kim said during the presentation that "we'll share more on Android XR at I/O next week."
It clearly seems like more is still to come, and not just for Android XR.
We didn't get confirmation on the Android Authority report that Google could add a more robust photo picker, with support for cloud storage solutions.
That doesn't mean it won't be in Android 16, it might just be something the company didn't get to mention in its 30-minute showcase.
Plus, Google has been releasing new Android features in a quarterly cadence lately, rather than wait till an annual update window to make updates available.
It's possible we see more added to Android 16 as the year progresses.
One of the best places to get an idea for what's to come in Android 16 is in its beta version, which has already been available to developers and is currently in its fourth iteration.
For example, we learned in March that Android 16 will bring Auracast support, which could make it easier to listen to and switch between multiple Bluetooth devices.
This could also enable people to receive Bluetooth audio on hearing aids they have paired with their phones or tablets.
Android XR
Remember Google Glass? No? How about Daydream? Maybe Cardboard? After sending (at least) three XR projects to the graveyard, you would think even Google would say enough is enough.
Instead, the company is preparing to release Android XR after previewing the platform at the end of last year.
This time around, the company says the power of its Gemini AI models will make things different.
We know Google is working with Samsung on a headset codenamed Project Moohan.
Last fall, Samsung hinted that the device could arrive sometime this year.
Whether Google and Samsung demo Project Moohan at I/O, I imagine the search giant will have more to say about Android XR and the ecosystem partners it has worked to bring to its side for the initiative.
This falls in line with what Kim said about more on Android XR being shared at I/O.
AI, AI and more AI
If Google felt the need to split off Android into its own showcase, we're likely to get more AI-related announcements at I/O than ever before.
The company hasn't provided many hints about what we can expect on that front, but if I had to guess, features like AI Overviews and AI Mode are likely to get substantive updates.
I suspect Google will also have something to say about Project Mariner, the web-surfing agent it demoed at I/O 2024.
Either way, Google is an AI company now, and every I/O moving forward will reflect that.
Project Astra
Speaking of AI, Project Astra was one of the more impressive demos Google showed off at I/O 2024.
The technology made the most of the latest multi-modal capabilities of Google's Gemini models to offer something we hadn't seen before from the company.
It's a voice assistant with advanced image recognition features that allows it to converse about the things it sees.
Google envisions Project Astra one day providing a truly useful artificial assistant.
However, after seeing an in-person demo of Astra, the Engadget crew felt the tech needed a lot more work.
Given the splash Project Astra made last year, there's a good chance we could get an update on it at I/O 2025.
A Pinterest competitor
According to a report from The Information, Google might be planning to unveil its own take on Pinterest next week.
That characterization is courtesy ofThe Information, but based on the features described in the article, Engadget team members found it more reminiscent of Cosmos instead.
Cosmos is a pared-down version of Pinterest, letting people save and curate anything they see on the internet.
It also allows you to share your saved pages with others.
Google's version, meanwhile, will reportedly show image results based on your queries, and you can save the pictures in different folders based on your own preferences.
So say you're putting together a lookbook based on Jennie from Blackpink.
You can search for her outfits and save your favorites in a folder you can title "Lewks," perhaps.
Whether this is simply built into Search or exists as a standalone product is unclear, and we'll have to wait till I/O to see whether the report was accurate and what the feature really is like.
Wear OS
Last year, Wear OS didn't get a mention during the company's main keynote, but Google did preview Wear OS 5 during the developer sessions that followed.
The company only began rolling out Wear OS 5.1 to Pixel devices in March.
This year, we've already learned at the Android Show that Wear OS 6 is coming, with Material 3 Expressive gracing its interface.
Will we learn more at I/O next week? It's unclear, but it wouldn't be a shock if that was all the air time Wear OS gets this year.
NotebookLM
Since 2023, Google has offered NotebookLM on desktop.
The note-taking app uses machine learning for features like automated summaries.
Based on App Store and Google Play listings, the company is getting ready to release a mobile version of the service on the first day of I/O 2025.
Everything else
Google has a terrible track record when it comes to preventing leaks within its internal ranks, so the likelihood the company could surprise us is low.
Still, Google could announce something we don't expect.
As always, your best bet is to visit Engadget on May 20 and 21.
We'll have all the latest from Google then along with our liveblog and analysis.
Update, May 5 2025, 7:08PM ET: This story has been updated to include details on a leaked blog post discussing "Material 3 Expressive."
Update, May 6 2025, 5:29PM ET: This story has been updated to include details on the Android 16 beta, as well as Auracast support.
Update, May 8 2025, 3:20PM ET: This story has been updated to include details on how to watch the Android Show and the Google I/O keynote, as well as tweak the intro for freshness.
Update, May 13 2025, 3:22PM ET: This story has been updated to include all the announcements from the Android Show and a new report from The Information about a possible image search feature debuting at I/O.
The intro was also edited to accurately reflect what has happened since the last time this article was updated.This article originally appeared on Engadget at https://www.engadget.com/ai/google-io-2025-what-to-expect-including-gemini-ai-android-16-updates-android-xr-and-more-203044563.html?src=rss" style="color: #0066cc;">https://www.engadget.com/ai/google-io-2025-what-to-expect-including-gemini-ai-android-16-updates-android-xr-and-more-203044563.html?src=rss
Source: https://www.engadget.com/ai/google-io-2025-what-to-expect-including-gemini-ai-android-16-updates-android-xr-and-more-203044563.html?src=rss">https://www.engadget.com/ai/google-io-2025-what-to-expect-including-gemini-ai-android-16-updates-android-xr-and-more-203044563.html?src=rss">https://www.engadget.com/ai/google-io-2025-what-to-expect-including-gemini-ai-android-16-updates-android-xr-and-more-203044563.html?src=rss
#google #what #expect #including #gemini #android #updates #and #more

Google I/O 2025: What to expect including Gemini AI, Android 16 updates, Android XR and more
In just a week, Google's annual developer conference will kick off on May 20. The event is probably the most important on the company's calendar, offering a glimpse at everything it has been working on over the past year. From the rumors and even information Google has trickled out, I/O 2025 should be one of the more exciting tech keynotes in recent memory. Plus, for the first time, Google has spun out a dedicated Android showcase planned a whole week earlier. That just happened today (May 13), and you can check out everything that was announced at the Android Show or go to our liveblog to get a feel for how things played out. Now that the Android Show is over, it's time to look ahead to I/O, where the focus will almost definitely be about AI. We've gathered the most credible reports and leaks to put together this roundup of what to expect, and though most of the Android-related announcements have been made, it's still possible that Google shares more details about its mobile platform next week. If you'd like to tune in from home and follow along as Google makes its announcements, check out our article on how to watch the Google I/O 2025 keynote. We'll also be liveblogging the event, so you can just come to Engadget for the breaking news. Android 16 Some of my favorite I/O moments involved watching Dave Burke take to the Shoreline stage to talk about the latest updates for Android. But for the past couple of years, Android hasn't had much of a spotlight at Google's annual developer conference. That's about to change, with the company's dedicated showcase during today's Android Show: I/O Edition. Get a front row seat to The Android Show: I/O Edition 🍿 May 13, 10 AM PT. Meet our team and learn about new experiences coming to Android. Set a reminder and be the first in the know → https://t.co/z8QLNSYkl6 #TheAndroidShow pic.twitter.com/RTzTOwUtFN— Android (@Android) April 28, 2025 The presentation featured Android Ecosystem President Sameer Samat, who took over for Burke in 2024. We saw Samat and his colleagues show off the new Material 3 Expressive design, and what we learned confirmed some of the features that were previously leaked, like the "Ongoing notifications" bar. Material 3 Expressive is also coming to Wear OS 6, and the company is expanding the reach of Gemini by bringing it to its smartwatch platform, Android Auto and Google TV. Android 16 will also come with new scam-detection features and a refined Find Hub that will see support for satellite connectivity later in the year. Speaking of timing, Google has already confirmed the new operating system will arrive sometime before the second half of the year. Though it did not release a stable build of Android 16 today, Samat shared during the show that Android 16 (or at least part of it) is coming next month to Pixel devices. And though the company did cover some new features coming to Android XR, senior director for Android Product and UX Guemmy Kim said during the presentation that "we'll share more on Android XR at I/O next week." It clearly seems like more is still to come, and not just for Android XR. We didn't get confirmation on the Android Authority report that Google could add a more robust photo picker, with support for cloud storage solutions. That doesn't mean it won't be in Android 16, it might just be something the company didn't get to mention in its 30-minute showcase. Plus, Google has been releasing new Android features in a quarterly cadence lately, rather than wait till an annual update window to make updates available. It's possible we see more added to Android 16 as the year progresses. One of the best places to get an idea for what's to come in Android 16 is in its beta version, which has already been available to developers and is currently in its fourth iteration. For example, we learned in March that Android 16 will bring Auracast support, which could make it easier to listen to and switch between multiple Bluetooth devices. This could also enable people to receive Bluetooth audio on hearing aids they have paired with their phones or tablets. Android XR Remember Google Glass? No? How about Daydream? Maybe Cardboard? After sending (at least) three XR projects to the graveyard, you would think even Google would say enough is enough. Instead, the company is preparing to release Android XR after previewing the platform at the end of last year. This time around, the company says the power of its Gemini AI models will make things different. We know Google is working with Samsung on a headset codenamed Project Moohan. Last fall, Samsung hinted that the device could arrive sometime this year. Whether Google and Samsung demo Project Moohan at I/O, I imagine the search giant will have more to say about Android XR and the ecosystem partners it has worked to bring to its side for the initiative. This falls in line with what Kim said about more on Android XR being shared at I/O. AI, AI and more AI If Google felt the need to split off Android into its own showcase, we're likely to get more AI-related announcements at I/O than ever before. The company hasn't provided many hints about what we can expect on that front, but if I had to guess, features like AI Overviews and AI Mode are likely to get substantive updates. I suspect Google will also have something to say about Project Mariner, the web-surfing agent it demoed at I/O 2024. Either way, Google is an AI company now, and every I/O moving forward will reflect that. Project Astra Speaking of AI, Project Astra was one of the more impressive demos Google showed off at I/O 2024. The technology made the most of the latest multi-modal capabilities of Google's Gemini models to offer something we hadn't seen before from the company. It's a voice assistant with advanced image recognition features that allows it to converse about the things it sees. Google envisions Project Astra one day providing a truly useful artificial assistant. However, after seeing an in-person demo of Astra, the Engadget crew felt the tech needed a lot more work. Given the splash Project Astra made last year, there's a good chance we could get an update on it at I/O 2025. A Pinterest competitor According to a report from The Information, Google might be planning to unveil its own take on Pinterest next week. That characterization is courtesy ofThe Information, but based on the features described in the article, Engadget team members found it more reminiscent of Cosmos instead. Cosmos is a pared-down version of Pinterest, letting people save and curate anything they see on the internet. It also allows you to share your saved pages with others. Google's version, meanwhile, will reportedly show image results based on your queries, and you can save the pictures in different folders based on your own preferences. So say you're putting together a lookbook based on Jennie from Blackpink. You can search for her outfits and save your favorites in a folder you can title "Lewks," perhaps. Whether this is simply built into Search or exists as a standalone product is unclear, and we'll have to wait till I/O to see whether the report was accurate and what the feature really is like. Wear OS Last year, Wear OS didn't get a mention during the company's main keynote, but Google did preview Wear OS 5 during the developer sessions that followed. The company only began rolling out Wear OS 5.1 to Pixel devices in March. This year, we've already learned at the Android Show that Wear OS 6 is coming, with Material 3 Expressive gracing its interface. Will we learn more at I/O next week? It's unclear, but it wouldn't be a shock if that was all the air time Wear OS gets this year. NotebookLM Since 2023, Google has offered NotebookLM on desktop. The note-taking app uses machine learning for features like automated summaries. Based on App Store and Google Play listings, the company is getting ready to release a mobile version of the service on the first day of I/O 2025. Everything else Google has a terrible track record when it comes to preventing leaks within its internal ranks, so the likelihood the company could surprise us is low. Still, Google could announce something we don't expect. As always, your best bet is to visit Engadget on May 20 and 21. We'll have all the latest from Google then along with our liveblog and analysis. Update, May 5 2025, 7:08PM ET: This story has been updated to include details on a leaked blog post discussing "Material 3 Expressive." Update, May 6 2025, 5:29PM ET: This story has been updated to include details on the Android 16 beta, as well as Auracast support. Update, May 8 2025, 3:20PM ET: This story has been updated to include details on how to watch the Android Show and the Google I/O keynote, as well as tweak the intro for freshness. Update, May 13 2025, 3:22PM ET: This story has been updated to include all the announcements from the Android Show and a new report from The Information about a possible image search feature debuting at I/O. The intro was also edited to accurately reflect what has happened since the last time this article was updated.This article originally appeared on Engadget at https://www.engadget.com/ai/google-io-2025-what-to-expect-including-gemini-ai-android-16-updates-android-xr-and-more-203044563.html?src=rss Source: https://www.engadget.com/ai/google-io-2025-what-to-expect-including-gemini-ai-android-16-updates-android-xr-and-more-203044563.html?src=rss #google #what #expect #including #gemini #android #updates #and #more

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Google I/O 2025: What to expect including Gemini AI, Android 16 updates, Android XR and more

In just a week, Google's annual developer conference will kick off on May 20. The event is probably the most important on the company's calendar, offering a glimpse at everything it has been working on over the past year. From the rumors and even information Google has trickled out, I/O 2025 should be one of the more exciting tech keynotes in recent memory. Plus, for the first time, Google has spun out a dedicated Android showcase planned a whole week earlier. That just happened today (May 13), and you can check out everything that was announced at the Android Show or go to our liveblog to get a feel for how things played out. Now that the Android Show is over, it's time to look ahead to I/O, where the focus will almost definitely be about AI. We've gathered the most credible reports and leaks to put together this roundup of what to expect, and though most of the Android-related announcements have been made, it's still possible that Google shares more details about its mobile platform next week. If you'd like to tune in from home and follow along as Google makes its announcements, check out our article on how to watch the Google I/O 2025 keynote. We'll also be liveblogging the event, so you can just come to Engadget for the breaking news. Android 16 Some of my favorite I/O moments involved watching Dave Burke take to the Shoreline stage to talk about the latest updates for Android. But for the past couple of years, Android hasn't had much of a spotlight at Google's annual developer conference. That's about to change, with the company's dedicated showcase during today's Android Show: I/O Edition. Get a front row seat to The Android Show: I/O Edition 🍿 May 13, 10 AM PT. Meet our team and learn about new experiences coming to Android. Set a reminder and be the first in the know → https://t.co/z8QLNSYkl6 #TheAndroidShow pic.twitter.com/RTzTOwUtFN— Android (@Android) April 28, 2025 The presentation featured Android Ecosystem President Sameer Samat, who took over for Burke in 2024. We saw Samat and his colleagues show off the new Material 3 Expressive design, and what we learned confirmed some of the features that were previously leaked, like the "Ongoing notifications" bar. Material 3 Expressive is also coming to Wear OS 6, and the company is expanding the reach of Gemini by bringing it to its smartwatch platform, Android Auto and Google TV. Android 16 will also come with new scam-detection features and a refined Find Hub that will see support for satellite connectivity later in the year. Speaking of timing, Google has already confirmed the new operating system will arrive sometime before the second half of the year. Though it did not release a stable build of Android 16 today, Samat shared during the show that Android 16 (or at least part of it) is coming next month to Pixel devices. And though the company did cover some new features coming to Android XR, senior director for Android Product and UX Guemmy Kim said during the presentation that "we'll share more on Android XR at I/O next week." It clearly seems like more is still to come, and not just for Android XR. We didn't get confirmation on the Android Authority report that Google could add a more robust photo picker, with support for cloud storage solutions. That doesn't mean it won't be in Android 16, it might just be something the company didn't get to mention in its 30-minute showcase. Plus, Google has been releasing new Android features in a quarterly cadence lately, rather than wait till an annual update window to make updates available. It's possible we see more added to Android 16 as the year progresses. One of the best places to get an idea for what's to come in Android 16 is in its beta version, which has already been available to developers and is currently in its fourth iteration. For example, we learned in March that Android 16 will bring Auracast support, which could make it easier to listen to and switch between multiple Bluetooth devices. This could also enable people to receive Bluetooth audio on hearing aids they have paired with their phones or tablets. Android XR Remember Google Glass? No? How about Daydream? Maybe Cardboard? After sending (at least) three XR projects to the graveyard, you would think even Google would say enough is enough. Instead, the company is preparing to release Android XR after previewing the platform at the end of last year. This time around, the company says the power of its Gemini AI models will make things different. We know Google is working with Samsung on a headset codenamed Project Moohan. Last fall, Samsung hinted that the device could arrive sometime this year. Whether Google and Samsung demo Project Moohan at I/O, I imagine the search giant will have more to say about Android XR and the ecosystem partners it has worked to bring to its side for the initiative. This falls in line with what Kim said about more on Android XR being shared at I/O. AI, AI and more AI If Google felt the need to split off Android into its own showcase, we're likely to get more AI-related announcements at I/O than ever before. The company hasn't provided many hints about what we can expect on that front, but if I had to guess, features like AI Overviews and AI Mode are likely to get substantive updates. I suspect Google will also have something to say about Project Mariner, the web-surfing agent it demoed at I/O 2024. Either way, Google is an AI company now, and every I/O moving forward will reflect that. Project Astra Speaking of AI, Project Astra was one of the more impressive demos Google showed off at I/O 2024. The technology made the most of the latest multi-modal capabilities of Google's Gemini models to offer something we hadn't seen before from the company. It's a voice assistant with advanced image recognition features that allows it to converse about the things it sees. Google envisions Project Astra one day providing a truly useful artificial assistant. However, after seeing an in-person demo of Astra, the Engadget crew felt the tech needed a lot more work. Given the splash Project Astra made last year, there's a good chance we could get an update on it at I/O 2025. A Pinterest competitor According to a report from The Information, Google might be planning to unveil its own take on Pinterest next week. That characterization is courtesy ofThe Information, but based on the features described in the article, Engadget team members found it more reminiscent of Cosmos instead. Cosmos is a pared-down version of Pinterest, letting people save and curate anything they see on the internet. It also allows you to share your saved pages with others. Google's version, meanwhile, will reportedly show image results based on your queries, and you can save the pictures in different folders based on your own preferences. So say you're putting together a lookbook based on Jennie from Blackpink. You can search for her outfits and save your favorites in a folder you can title "Lewks," perhaps. Whether this is simply built into Search or exists as a standalone product is unclear, and we'll have to wait till I/O to see whether the report was accurate and what the feature really is like. Wear OS Last year, Wear OS didn't get a mention during the company's main keynote, but Google did preview Wear OS 5 during the developer sessions that followed. The company only began rolling out Wear OS 5.1 to Pixel devices in March. This year, we've already learned at the Android Show that Wear OS 6 is coming, with Material 3 Expressive gracing its interface. Will we learn more at I/O next week? It's unclear, but it wouldn't be a shock if that was all the air time Wear OS gets this year. NotebookLM Since 2023, Google has offered NotebookLM on desktop. The note-taking app uses machine learning for features like automated summaries. Based on App Store and Google Play listings, the company is getting ready to release a mobile version of the service on the first day of I/O 2025. Everything else Google has a terrible track record when it comes to preventing leaks within its internal ranks, so the likelihood the company could surprise us is low. Still, Google could announce something we don't expect. As always, your best bet is to visit Engadget on May 20 and 21. We'll have all the latest from Google then along with our liveblog and analysis. Update, May 5 2025, 7:08PM ET: This story has been updated to include details on a leaked blog post discussing "Material 3 Expressive." Update, May 6 2025, 5:29PM ET: This story has been updated to include details on the Android 16 beta, as well as Auracast support. Update, May 8 2025, 3:20PM ET: This story has been updated to include details on how to watch the Android Show and the Google I/O keynote, as well as tweak the intro for freshness. Update, May 13 2025, 3:22PM ET: This story has been updated to include all the announcements from the Android Show and a new report from The Information about a possible image search feature debuting at I/O. The intro was also edited to accurately reflect what has happened since the last time this article was updated.This article originally appeared on Engadget at https://www.engadget.com/ai/google-io-2025-what-to-expect-including-gemini-ai-android-16-updates-android-xr-and-more-203044563.html?src=rss

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2025-05-14 09:11:22 ·

Republicans push for a decadelong ban on states regulating AI
Republicans want to stop states from regulating AI.
On Sunday, a Republican-led House committee submitted a budget reconciliation bill that proposes blocking states from enforcing “any law or regulation” targeting an exceptionally broad range of automated computing systems for 10 years after the law is enacted — a move that would stall efforts to regulate everything from AI chatbots to online search results.
Democrats are calling the new provision a “giant gift” to Big Tech, and organizations that promote AI oversight, like Americans for Responsible Innovation (ARI), say it could have “catastrophic consequences” for the public.
It’s a gift companies like OpenAI have recently been seeking in Washington, aiming to avoid a slew of pending and active state laws.
The budget reconciliation process allows lawmakers to fast-track bills related to government spending by requiring only a majority in the Senate rather than 60 votes to pass.
This bill, introduced by House Committee on Energy and Commerce Chairman Brett Guthrie (R-KY), would prevent states from imposing “legal impediments” — or restrictions to design, performance, civil liability, and documentation — on AI models and “automated decision” systems.
It defines the latter category as “any computational process derived from machine learning, statistical modeling, data analytics, or artificial intelligence that issues a simplified output, including a score, classification, or recommendation, to materially influence or replace human decision making.”That means the 10-year moratorium could extend well beyond AI.
Travis Hall, the director for state engagement at the Center for Democracy & Technology, tells The Verge that the automated decision systems described in the bill “permeate digital services, from search results and mapping directions, to health diagnoses and risk analyses for sentencing decisions.”During the 2025 legislative session, states have proposed over 500 laws that Hall says this bill could “unequivocally block.” They focus on everything from chatbot safety for minors to deepfake restrictions and disclosures for the use of AI in political ads.
If the bill passes, the handful of states that have successfully passed AI laws may also see their efforts go to waste.
“The move to ban AI safeguards is a giveaway to Big Tech that will come back to bite us.”Last year, California Gov.
Gavin Newsom signed a law preventing companies from using a performer’s AI-generated likeness without permission.
Tennessee also adopted legislation with similar protections, while Utah has enacted a rule requiring certain businesses to disclose when customers are interacting with AI.
Colorado’s AI law, which goes into effect next year, will require companies developing “high-risk” AI systems to protect customers from “algorithmic discrimination.”California also came close to enacting the landmark AI safety law SB 1047, which would have imposed security restrictions and legal liability on AI companies based in the state, like OpenAI, Anthropic, Google, and Meta.
OpenAI opposed the bill, saying AI regulation should take place at the federal level instead of having a “patchwork” of state laws that could make it more difficult to comply.
Gov.
Newsom vetoed the bill last September, and OpenAI has made it clear it wants to avoid having state laws “bogging down innovation” in the future.With so little AI regulation at the federal level, it’s been left up to the states to decide how to deal with AI.
Even before the rise of generative AI, state legislators were grappling with how to fight algorithmic discrimination — including machine learning-based systems that display race or gender bias — in areas like housing and criminal justice.
Efforts to combat this, too, would likely be hampered by the Republicans’ proposal.Democrats have slammed the provision’s inclusion in the reconciliation bill, with Rep.
Jan Schakowsky (D-IL) saying the 10-year ban will “allow AI companies to ignore consumer privacy protections, let deepfakes spread, and allow companies to profile and deceive consumers using AI.” In a statement published to X, Sen.
Ed Markey (D-MA) said the proposal “will lead to a Dark Age for the environment, our children, and marginalized communities.”RelatedThe nonprofit organization Americans for Responsible Innovation (ARI) compared the potential ban to the government’s failure to properly regulate social media.
“Lawmakers stalled on social media safeguards for a decade and we are still dealing with the fallout,” ARI president Brad Carson said in a statement.
“Now apply those same harms to technology moving as fast as AI… Ultimately, the move to ban AI safeguards is a giveaway to Big Tech that will come back to bite us.”This provision could hit a roadblock in the Senate, as ARI notes that the Byrd rule says reconciliation bills can only focus on fiscal issues.
Still, it’s troubling to see Republican lawmakers push to block oversight of a new technology that’s being integrated into almost everything.See More:
Source: https://www.theverge.com/news/666288/republican-ai-state-regulation-ban-10-years" style="color: #0066cc;">https://www.theverge.com/news/666288/republican-ai-state-regulation-ban-10-years
#republicans #push #for #decadelong #ban #states #regulating

Republicans push for a decadelong ban on states regulating AI
Republicans want to stop states from regulating AI. On Sunday, a Republican-led House committee submitted a budget reconciliation bill that proposes blocking states from enforcing “any law or regulation” targeting an exceptionally broad range of automated computing systems for 10 years after the law is enacted — a move that would stall efforts to regulate everything from AI chatbots to online search results. Democrats are calling the new provision a “giant gift” to Big Tech, and organizations that promote AI oversight, like Americans for Responsible Innovation (ARI), say it could have “catastrophic consequences” for the public. It’s a gift companies like OpenAI have recently been seeking in Washington, aiming to avoid a slew of pending and active state laws. The budget reconciliation process allows lawmakers to fast-track bills related to government spending by requiring only a majority in the Senate rather than 60 votes to pass. This bill, introduced by House Committee on Energy and Commerce Chairman Brett Guthrie (R-KY), would prevent states from imposing “legal impediments” — or restrictions to design, performance, civil liability, and documentation — on AI models and “automated decision” systems. It defines the latter category as “any computational process derived from machine learning, statistical modeling, data analytics, or artificial intelligence that issues a simplified output, including a score, classification, or recommendation, to materially influence or replace human decision making.”That means the 10-year moratorium could extend well beyond AI. Travis Hall, the director for state engagement at the Center for Democracy & Technology, tells The Verge that the automated decision systems described in the bill “permeate digital services, from search results and mapping directions, to health diagnoses and risk analyses for sentencing decisions.”During the 2025 legislative session, states have proposed over 500 laws that Hall says this bill could “unequivocally block.” They focus on everything from chatbot safety for minors to deepfake restrictions and disclosures for the use of AI in political ads. If the bill passes, the handful of states that have successfully passed AI laws may also see their efforts go to waste. “The move to ban AI safeguards is a giveaway to Big Tech that will come back to bite us.”Last year, California Gov. Gavin Newsom signed a law preventing companies from using a performer’s AI-generated likeness without permission. Tennessee also adopted legislation with similar protections, while Utah has enacted a rule requiring certain businesses to disclose when customers are interacting with AI. Colorado’s AI law, which goes into effect next year, will require companies developing “high-risk” AI systems to protect customers from “algorithmic discrimination.”California also came close to enacting the landmark AI safety law SB 1047, which would have imposed security restrictions and legal liability on AI companies based in the state, like OpenAI, Anthropic, Google, and Meta. OpenAI opposed the bill, saying AI regulation should take place at the federal level instead of having a “patchwork” of state laws that could make it more difficult to comply. Gov. Newsom vetoed the bill last September, and OpenAI has made it clear it wants to avoid having state laws “bogging down innovation” in the future.With so little AI regulation at the federal level, it’s been left up to the states to decide how to deal with AI. Even before the rise of generative AI, state legislators were grappling with how to fight algorithmic discrimination — including machine learning-based systems that display race or gender bias — in areas like housing and criminal justice. Efforts to combat this, too, would likely be hampered by the Republicans’ proposal.Democrats have slammed the provision’s inclusion in the reconciliation bill, with Rep. Jan Schakowsky (D-IL) saying the 10-year ban will “allow AI companies to ignore consumer privacy protections, let deepfakes spread, and allow companies to profile and deceive consumers using AI.” In a statement published to X, Sen. Ed Markey (D-MA) said the proposal “will lead to a Dark Age for the environment, our children, and marginalized communities.”RelatedThe nonprofit organization Americans for Responsible Innovation (ARI) compared the potential ban to the government’s failure to properly regulate social media. “Lawmakers stalled on social media safeguards for a decade and we are still dealing with the fallout,” ARI president Brad Carson said in a statement. “Now apply those same harms to technology moving as fast as AI… Ultimately, the move to ban AI safeguards is a giveaway to Big Tech that will come back to bite us.”This provision could hit a roadblock in the Senate, as ARI notes that the Byrd rule says reconciliation bills can only focus on fiscal issues. Still, it’s troubling to see Republican lawmakers push to block oversight of a new technology that’s being integrated into almost everything.See More: Source: https://www.theverge.com/news/666288/republican-ai-state-regulation-ban-10-years #republicans #push #for #decadelong #ban #states #regulating

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Republicans push for a decadelong ban on states regulating AI

Republicans want to stop states from regulating AI. On Sunday, a Republican-led House committee submitted a budget reconciliation bill that proposes blocking states from enforcing “any law or regulation” targeting an exceptionally broad range of automated computing systems for 10 years after the law is enacted — a move that would stall efforts to regulate everything from AI chatbots to online search results. Democrats are calling the new provision a “giant gift” to Big Tech, and organizations that promote AI oversight, like Americans for Responsible Innovation (ARI), say it could have “catastrophic consequences” for the public. It’s a gift companies like OpenAI have recently been seeking in Washington, aiming to avoid a slew of pending and active state laws. The budget reconciliation process allows lawmakers to fast-track bills related to government spending by requiring only a majority in the Senate rather than 60 votes to pass. This bill, introduced by House Committee on Energy and Commerce Chairman Brett Guthrie (R-KY), would prevent states from imposing “legal impediments” — or restrictions to design, performance, civil liability, and documentation — on AI models and “automated decision” systems. It defines the latter category as “any computational process derived from machine learning, statistical modeling, data analytics, or artificial intelligence that issues a simplified output, including a score, classification, or recommendation, to materially influence or replace human decision making.”That means the 10-year moratorium could extend well beyond AI. Travis Hall, the director for state engagement at the Center for Democracy & Technology, tells The Verge that the automated decision systems described in the bill “permeate digital services, from search results and mapping directions, to health diagnoses and risk analyses for sentencing decisions.”During the 2025 legislative session, states have proposed over 500 laws that Hall says this bill could “unequivocally block.” They focus on everything from chatbot safety for minors to deepfake restrictions and disclosures for the use of AI in political ads. If the bill passes, the handful of states that have successfully passed AI laws may also see their efforts go to waste. “The move to ban AI safeguards is a giveaway to Big Tech that will come back to bite us.”Last year, California Gov. Gavin Newsom signed a law preventing companies from using a performer’s AI-generated likeness without permission. Tennessee also adopted legislation with similar protections, while Utah has enacted a rule requiring certain businesses to disclose when customers are interacting with AI. Colorado’s AI law, which goes into effect next year, will require companies developing “high-risk” AI systems to protect customers from “algorithmic discrimination.”California also came close to enacting the landmark AI safety law SB 1047, which would have imposed security restrictions and legal liability on AI companies based in the state, like OpenAI, Anthropic, Google, and Meta. OpenAI opposed the bill, saying AI regulation should take place at the federal level instead of having a “patchwork” of state laws that could make it more difficult to comply. Gov. Newsom vetoed the bill last September, and OpenAI has made it clear it wants to avoid having state laws “bogging down innovation” in the future.With so little AI regulation at the federal level, it’s been left up to the states to decide how to deal with AI. Even before the rise of generative AI, state legislators were grappling with how to fight algorithmic discrimination — including machine learning-based systems that display race or gender bias — in areas like housing and criminal justice. Efforts to combat this, too, would likely be hampered by the Republicans’ proposal.Democrats have slammed the provision’s inclusion in the reconciliation bill, with Rep. Jan Schakowsky (D-IL) saying the 10-year ban will “allow AI companies to ignore consumer privacy protections, let deepfakes spread, and allow companies to profile and deceive consumers using AI.” In a statement published to X, Sen. Ed Markey (D-MA) said the proposal “will lead to a Dark Age for the environment, our children, and marginalized communities.”RelatedThe nonprofit organization Americans for Responsible Innovation (ARI) compared the potential ban to the government’s failure to properly regulate social media. “Lawmakers stalled on social media safeguards for a decade and we are still dealing with the fallout,” ARI president Brad Carson said in a statement. “Now apply those same harms to technology moving as fast as AI… Ultimately, the move to ban AI safeguards is a giveaway to Big Tech that will come back to bite us.”This provision could hit a roadblock in the Senate, as ARI notes that the Byrd rule says reconciliation bills can only focus on fiscal issues. Still, it’s troubling to see Republican lawmakers push to block oversight of a new technology that’s being integrated into almost everything.See More:

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2025-05-14 07:30:45 ·

What your tools miss at 2:13 AM: How gen AI attack chains exploit telemetry lag – Part 2

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Learn More
Generative AI is creating a digital diaspora of techniques, technologies and tradecraft that everyone, from rogue attackers to nation-state cyber armies trained in the art of cyberwar, is adopting.
Insider threats are growing, too, accelerated by job insecurity and growing inflation.
All these challenges and more fall on the shoulders of the CISO, and it’s no wonder more are dealing with burnout.
In Part 1:
We explored how gen AI is reshaping the threat landscape, accelerating insider threats and putting unprecedented pressure on cybersecurity teams.
Insider-driven risks, shadow AI usage and outdated detection models are forcing CISOs to rethink their defenses.Now, in Part 2, we turn to the solutions — how gen AI can help combat burnout across security operations centers (SOCs), enable smarter automation and guide CISOs through a 90-day roadmap to secure their enterprises against evolving threats.
Battling burnout with gen AI deserves to be a 2025 CISO priority
Nearly one in four CISOs consider quitting, with 93% citing extreme stress, further proving that burnout is creating increasingly severe operational and human risks.
Gartner’s most recent research links burnout to decreased team efficiency and overlooked security tasks that often become vulnerabilities.
Unsurprisingly, 90% of CISOs identify burnout as one of the main barriers that stand in the way of their teams getting more accomplished and using the full extent of their skills.
How bad is burnout across cybersecurity and SOC teams? The majority of CISOs, 65%, say that burnout is a severe impediment to maintaining effective security operations.
Forrester adds that 36% of the cybersecurity workforce are categorized as “Tired Rockstars,” or individuals who remain highly engaged but are on the brink of burnout.
This emphasizes the critical need to address mental health and workload management proactively.
SOC analysts endure heavy workloads that often turn severe when they have to monitor, analyze and aggregate insights from an average of over 10,000+ alerts a day.
Chronic stress and not having enough control over their jobs lead to high turnover, with 65% considering leaving their careers.
Ivanti’s 2024 Digital Employee Experience (DEX) Report underscores a vital cybersecurity link, noting that 93% of professionals agree improved DEX strengthens security, yet just 13% prioritize it.
Ivanti SVP Daren Goeson told VentureBeat in a recent interview that “organizations often lack effective tools to measure digital employee experience, significantly slowing security and productivity initiatives.”
SOC teams are particularly hard hit by burnout.
While AI can’t solve the entire challenge, it can help automate SOC workflows and accelerate triage.
Forrester is urging CISOs to think beyond automating existing processes and move forward with rationalizing security controls, deploying gen AI within existing platforms.
Jeff Pollard, VP at Forrester, writes: “The only way to deal with the volatility your organization encounters is to simplify your control stack while identifying unnecessary duplicate spend and gen AI can boost productivity, but negotiating its pricing strategically will help you achieve more with less.”
There are over 16 vendors of new-gen AI-based apps aimed at helping SOC teams that are in a race against time every day, especially when it comes to containing breakout times.
CrowdStrike’s recent global threat report emphasizes why SOCs need to always have their A-game, as adversaries now break out within 2 minutes and 7 seconds after gaining initial access.
Their recent introduction of Charlotte AI Detection Triage has proven capable of automating alert assessment with over 98% accuracy.
It cuts manual triage by more than 40 hours per week, all without losing control or precision.
SOCs increasingly lean on AI copilots to fight signal overload and staffing shortfalls.
VentureBeat’s Security Copilot Guide (Google Sheet) provides a complete matrix with 16 vendors’ AI security copilots.
What needs to be on every CISO’s roadmap in 2025
Cybersecurity leaders and their teams have significant influence on how, when and what gen AI applications and platforms their enterprises invest in.
Gartner’s Phillip Shattan writes that “when it comes to generation AI-related decisions, SRM leaders wield significant influence, with over 70% reporting that cybersecurity has some influence over the decisions they make.”
With so much influence on the future of gen AI investment in their organizations, CISOs need to have a solid framework or roadmap against which to plan.
VentureBeat is seeing more roadmaps comparable to the one structured below for ensuring the integration of gen AI, cybersecurity and risk management initiatives.
The following is a guideline that needs to be tailored to the unique needs of a business:
Days 0–30: Establish core cybersecurity foundations
1.
Set the goal of defining the structure and role of an AI governance framework
Define formal AI policies outlining responsible data use, model training protocols, privacy controls and ethical standards.

Vendors to consider: IBM AI Governance, Microsoft Purview, ServiceNow AI Governance, AWS AI Service Cards
If not already in place, deploy real-time AI monitoring tools to detect unauthorized usage, anomalous behaviors and data leakage from models.

Recommended platforms: Robust Intelligence, CalypsoAI, HiddenLayer, Arize AI, Credo AI, Arthur AI
Train SOC, security and risk management teams on the AI-specific risks to alleviate any conflicts over how AI governance frameworks are designed to work.

2.
If not already in place, get a solid Identity and Access Management (IAM) platform in place
Keep building a business case for zero trust by illustrating how improving identity protection helps protect and grow revenue.

Deploy a robust IAM solution to reinforce identity protection and revenue security.

Top IAM platforms:
If not already done, immediately conduct comprehensive audits of all user identities, focusing particularly on privileged access accounts.
Enable real-time monitoring for all privileged access accounts and delete unused accounts for contractors.
Implement strict least-privilege access policies, multi-factor authentication (MFA) and continuous adaptive authentication based on contextual risk assessments to strengthen your zero-trust framework.

Leading Zero-Trust solutions include CrowdStrike Falcon Identity Protection, Zscaler Zero Trust Exchange, Palo Alto Networks Prisma Access, Cisco Duo Security and Cloudflare Zero Trust.
Establish real-time monitoring and behavioral analytics to identify and reduce insider threats rapidly.
Insider threat detection leaders: Proofpoint Insider Threat Management, Varonis DatAdvantage, Forcepoint Insider Threat, DTEX Systems, Microsoft Purview Insider Risk Management.
Days 31–60: Accelerate Proactive Security Operations
1.
Replace manual patch workflows with an automated patch management systems
Your organization needs to move beyond fire drills and severity-based patch cycles to a continuous, real-time vulnerability monitoring and patch deployment strategy.
AI is helping cut the risks of breaches with patch management.
Six in ten breaches are linked to unpatched vulnerabilities.
The majority of IT leaders responding to a Ponemon Institute survey, 60%, say that one or more of the breaches potentially occurred because a patch was available for a known vulnerability but not applied in time.

Leading automated patch management vendors: Ivanti Neurons for Patch Management, Qualys Patch Management, Tanium Patch Management, CrowdStrike Falcon Spotlight, Rapid7 InsightVM.
Implement automated tools prioritizing patches based on active exploitation, threat intelligence insights and business-critical asset prioritization.
Establish transparent processes for immediate response to emerging threats, drastically reducing exposure windows.
2.
Initiate comprehensive Cyber Risk Quantification (CRQ)
If not already in progress in your organization, start evaluating the value of CRQ frameworks in improving how cybersecurity risks are measured and communicated in financial and business impact terms.
Trusted CRQ solutions: BitSight, SecurityScorecard, Axio360, RiskLens, MetricStream, Safe Security, IBM Security Risk Quantification Services.
Test out a CRQ by creating a detailed risk dashboard for executives and stakeholders, linking cybersecurity investments directly to strategic business outcomes.
Conduct regular CRQ assessments to inform proactive security spending and resource allocation decisions clearly and strategically.
Days 61–90: Keep optimizing security efficiency to fuel greater team resilience
1.
Consolidate and Integrate Security Tools
Audit existing cybersecurity tools, eliminating redundancies and streamlining capabilities into fewer, fully integrated platforms.

Comprehensive integrated platforms: Palo Alto Networks Cortex XDR, Microsoft Sentinel, CrowdStrike Falcon Platform, Splunk Security Cloud, Cisco SecureX, Trellix XDR, Arctic Wolf Security Operations Cloud.
Check for strong interoperability and reliable integration among cybersecurity tools to improve threat detection, response times and overall operational efficiency.
Regularly review and adjust consolidated toolsets based on evolving threat landscapes and organizational security needs.
2.
Implement structured burnout mitigation and automation
Starting in the SOC, leverage AI-driven automation to offload repetitive cybersecurity tasks, including triage, log analysis, vulnerability scanning and initial threat triage, significantly reducing manual workloads.

Recommended SOC automation tools: CrowdStrike Falcon Fusion, SentinelOne Singularity XDR, Microsoft Defender & Copilot, Palo Alto Networks Cortex XSOAR, Ivanti Neurons for Security Operations
Establish structured recovery protocols, mandating cooldown periods and rotation schedules after major cybersecurity incidents to reduce analyst fatigue.
Define a balanced, regular cadence of ongoing cybersecurity training, mental well-being initiatives, and institutionalized burnout mitigation practices to sustain long-term team resilience and efficiency.

Automation and burnout mitigation vendors: Tines, Torq.io, Swimlane, Chronicle Security Operations Suite (Google Cloud), LogicHub SOAR+, Palo Alto Networks Cortex XSOAR
Conclusion
With modest budget and headcount increases, CISOs and their teams are being called to defend more threat vectors than ever.
Many tell VentureBeat it’s a continual balancing act that demands more time, training, and trade-offs on which legacy apps stay and which go, all defining how their future tech stack will look.
CISOs who see gen AI as a strategic technology that can help unify and close gaps in security infrastructure are thorough in their vetting of new apps and tools before they go into production.
While gen AI continues to fuel new adversarial AI techniques and tradecraft, cybersecurity vendors respond by accelerating the development of next-generation products.
Paradoxically, the more advanced threatcraft becomes with adversarial AI, the more critical it becomes for defenders adopting AI to pursue and perfect human-in-the-middle designs that can flex and adapt to changing threats.
Daily insights on business use cases with VB Daily
If you want to impress your boss, VB Daily has you covered.
We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.
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Source: https://venturebeat.com/ai/what-your-tools-miss-at-213-am-how-gen-ai-attack-chains-exploit-telemetry-lag-part-2/" style="color: #0066cc;">https://venturebeat.com/ai/what-your-tools-miss-at-213-am-how-gen-ai-attack-chains-exploit-telemetry-lag-part-2/
#what #your #tools #miss #how #gen #attack #chains #exploit #telemetry #lag #part

What your tools miss at 2:13 AM: How gen AI attack chains exploit telemetry lag – Part 2
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Generative AI is creating a digital diaspora of techniques, technologies and tradecraft that everyone, from rogue attackers to nation-state cyber armies trained in the art of cyberwar, is adopting. Insider threats are growing, too, accelerated by job insecurity and growing inflation. All these challenges and more fall on the shoulders of the CISO, and it’s no wonder more are dealing with burnout. In Part 1: We explored how gen AI is reshaping the threat landscape, accelerating insider threats and putting unprecedented pressure on cybersecurity teams. Insider-driven risks, shadow AI usage and outdated detection models are forcing CISOs to rethink their defenses.Now, in Part 2, we turn to the solutions — how gen AI can help combat burnout across security operations centers (SOCs), enable smarter automation and guide CISOs through a 90-day roadmap to secure their enterprises against evolving threats. Battling burnout with gen AI deserves to be a 2025 CISO priority Nearly one in four CISOs consider quitting, with 93% citing extreme stress, further proving that burnout is creating increasingly severe operational and human risks. Gartner’s most recent research links burnout to decreased team efficiency and overlooked security tasks that often become vulnerabilities. Unsurprisingly, 90% of CISOs identify burnout as one of the main barriers that stand in the way of their teams getting more accomplished and using the full extent of their skills. How bad is burnout across cybersecurity and SOC teams? The majority of CISOs, 65%, say that burnout is a severe impediment to maintaining effective security operations. Forrester adds that 36% of the cybersecurity workforce are categorized as “Tired Rockstars,” or individuals who remain highly engaged but are on the brink of burnout. This emphasizes the critical need to address mental health and workload management proactively. SOC analysts endure heavy workloads that often turn severe when they have to monitor, analyze and aggregate insights from an average of over 10,000+ alerts a day. Chronic stress and not having enough control over their jobs lead to high turnover, with 65% considering leaving their careers. Ivanti’s 2024 Digital Employee Experience (DEX) Report underscores a vital cybersecurity link, noting that 93% of professionals agree improved DEX strengthens security, yet just 13% prioritize it. Ivanti SVP Daren Goeson told VentureBeat in a recent interview that “organizations often lack effective tools to measure digital employee experience, significantly slowing security and productivity initiatives.” SOC teams are particularly hard hit by burnout. While AI can’t solve the entire challenge, it can help automate SOC workflows and accelerate triage. Forrester is urging CISOs to think beyond automating existing processes and move forward with rationalizing security controls, deploying gen AI within existing platforms. Jeff Pollard, VP at Forrester, writes: “The only way to deal with the volatility your organization encounters is to simplify your control stack while identifying unnecessary duplicate spend and gen AI can boost productivity, but negotiating its pricing strategically will help you achieve more with less.” There are over 16 vendors of new-gen AI-based apps aimed at helping SOC teams that are in a race against time every day, especially when it comes to containing breakout times. CrowdStrike’s recent global threat report emphasizes why SOCs need to always have their A-game, as adversaries now break out within 2 minutes and 7 seconds after gaining initial access. Their recent introduction of Charlotte AI Detection Triage has proven capable of automating alert assessment with over 98% accuracy. It cuts manual triage by more than 40 hours per week, all without losing control or precision. SOCs increasingly lean on AI copilots to fight signal overload and staffing shortfalls. VentureBeat’s Security Copilot Guide (Google Sheet) provides a complete matrix with 16 vendors’ AI security copilots. What needs to be on every CISO’s roadmap in 2025 Cybersecurity leaders and their teams have significant influence on how, when and what gen AI applications and platforms their enterprises invest in. Gartner’s Phillip Shattan writes that “when it comes to generation AI-related decisions, SRM leaders wield significant influence, with over 70% reporting that cybersecurity has some influence over the decisions they make.” With so much influence on the future of gen AI investment in their organizations, CISOs need to have a solid framework or roadmap against which to plan. VentureBeat is seeing more roadmaps comparable to the one structured below for ensuring the integration of gen AI, cybersecurity and risk management initiatives. The following is a guideline that needs to be tailored to the unique needs of a business: Days 0–30: Establish core cybersecurity foundations 1. Set the goal of defining the structure and role of an AI governance framework Define formal AI policies outlining responsible data use, model training protocols, privacy controls and ethical standards. Vendors to consider: IBM AI Governance, Microsoft Purview, ServiceNow AI Governance, AWS AI Service Cards If not already in place, deploy real-time AI monitoring tools to detect unauthorized usage, anomalous behaviors and data leakage from models. Recommended platforms: Robust Intelligence, CalypsoAI, HiddenLayer, Arize AI, Credo AI, Arthur AI Train SOC, security and risk management teams on the AI-specific risks to alleviate any conflicts over how AI governance frameworks are designed to work. 2. If not already in place, get a solid Identity and Access Management (IAM) platform in place Keep building a business case for zero trust by illustrating how improving identity protection helps protect and grow revenue. Deploy a robust IAM solution to reinforce identity protection and revenue security. Top IAM platforms: If not already done, immediately conduct comprehensive audits of all user identities, focusing particularly on privileged access accounts. Enable real-time monitoring for all privileged access accounts and delete unused accounts for contractors. Implement strict least-privilege access policies, multi-factor authentication (MFA) and continuous adaptive authentication based on contextual risk assessments to strengthen your zero-trust framework. Leading Zero-Trust solutions include CrowdStrike Falcon Identity Protection, Zscaler Zero Trust Exchange, Palo Alto Networks Prisma Access, Cisco Duo Security and Cloudflare Zero Trust. Establish real-time monitoring and behavioral analytics to identify and reduce insider threats rapidly. Insider threat detection leaders: Proofpoint Insider Threat Management, Varonis DatAdvantage, Forcepoint Insider Threat, DTEX Systems, Microsoft Purview Insider Risk Management. Days 31–60: Accelerate Proactive Security Operations 1. Replace manual patch workflows with an automated patch management systems Your organization needs to move beyond fire drills and severity-based patch cycles to a continuous, real-time vulnerability monitoring and patch deployment strategy. AI is helping cut the risks of breaches with patch management. Six in ten breaches are linked to unpatched vulnerabilities. The majority of IT leaders responding to a Ponemon Institute survey, 60%, say that one or more of the breaches potentially occurred because a patch was available for a known vulnerability but not applied in time. Leading automated patch management vendors: Ivanti Neurons for Patch Management, Qualys Patch Management, Tanium Patch Management, CrowdStrike Falcon Spotlight, Rapid7 InsightVM. Implement automated tools prioritizing patches based on active exploitation, threat intelligence insights and business-critical asset prioritization. Establish transparent processes for immediate response to emerging threats, drastically reducing exposure windows. 2. Initiate comprehensive Cyber Risk Quantification (CRQ) If not already in progress in your organization, start evaluating the value of CRQ frameworks in improving how cybersecurity risks are measured and communicated in financial and business impact terms. Trusted CRQ solutions: BitSight, SecurityScorecard, Axio360, RiskLens, MetricStream, Safe Security, IBM Security Risk Quantification Services. Test out a CRQ by creating a detailed risk dashboard for executives and stakeholders, linking cybersecurity investments directly to strategic business outcomes. Conduct regular CRQ assessments to inform proactive security spending and resource allocation decisions clearly and strategically. Days 61–90: Keep optimizing security efficiency to fuel greater team resilience 1. Consolidate and Integrate Security Tools Audit existing cybersecurity tools, eliminating redundancies and streamlining capabilities into fewer, fully integrated platforms. Comprehensive integrated platforms: Palo Alto Networks Cortex XDR, Microsoft Sentinel, CrowdStrike Falcon Platform, Splunk Security Cloud, Cisco SecureX, Trellix XDR, Arctic Wolf Security Operations Cloud. Check for strong interoperability and reliable integration among cybersecurity tools to improve threat detection, response times and overall operational efficiency. Regularly review and adjust consolidated toolsets based on evolving threat landscapes and organizational security needs. 2. Implement structured burnout mitigation and automation Starting in the SOC, leverage AI-driven automation to offload repetitive cybersecurity tasks, including triage, log analysis, vulnerability scanning and initial threat triage, significantly reducing manual workloads. Recommended SOC automation tools: CrowdStrike Falcon Fusion, SentinelOne Singularity XDR, Microsoft Defender & Copilot, Palo Alto Networks Cortex XSOAR, Ivanti Neurons for Security Operations Establish structured recovery protocols, mandating cooldown periods and rotation schedules after major cybersecurity incidents to reduce analyst fatigue. Define a balanced, regular cadence of ongoing cybersecurity training, mental well-being initiatives, and institutionalized burnout mitigation practices to sustain long-term team resilience and efficiency. Automation and burnout mitigation vendors: Tines, Torq.io, Swimlane, Chronicle Security Operations Suite (Google Cloud), LogicHub SOAR+, Palo Alto Networks Cortex XSOAR Conclusion With modest budget and headcount increases, CISOs and their teams are being called to defend more threat vectors than ever. Many tell VentureBeat it’s a continual balancing act that demands more time, training, and trade-offs on which legacy apps stay and which go, all defining how their future tech stack will look. CISOs who see gen AI as a strategic technology that can help unify and close gaps in security infrastructure are thorough in their vetting of new apps and tools before they go into production. While gen AI continues to fuel new adversarial AI techniques and tradecraft, cybersecurity vendors respond by accelerating the development of next-generation products. Paradoxically, the more advanced threatcraft becomes with adversarial AI, the more critical it becomes for defenders adopting AI to pursue and perfect human-in-the-middle designs that can flex and adapt to changing threats. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured. Source: https://venturebeat.com/ai/what-your-tools-miss-at-213-am-how-gen-ai-attack-chains-exploit-telemetry-lag-part-2/ #what #your #tools #miss #how #gen #attack #chains #exploit #telemetry #lag #part

VENTUREBEAT.COM

What your tools miss at 2:13 AM: How gen AI attack chains exploit telemetry lag – Part 2

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Generative AI is creating a digital diaspora of techniques, technologies and tradecraft that everyone, from rogue attackers to nation-state cyber armies trained in the art of cyberwar, is adopting. Insider threats are growing, too, accelerated by job insecurity and growing inflation. All these challenges and more fall on the shoulders of the CISO, and it’s no wonder more are dealing with burnout. In Part 1: We explored how gen AI is reshaping the threat landscape, accelerating insider threats and putting unprecedented pressure on cybersecurity teams. Insider-driven risks, shadow AI usage and outdated detection models are forcing CISOs to rethink their defenses.Now, in Part 2, we turn to the solutions — how gen AI can help combat burnout across security operations centers (SOCs), enable smarter automation and guide CISOs through a 90-day roadmap to secure their enterprises against evolving threats. Battling burnout with gen AI deserves to be a 2025 CISO priority Nearly one in four CISOs consider quitting, with 93% citing extreme stress, further proving that burnout is creating increasingly severe operational and human risks. Gartner’s most recent research links burnout to decreased team efficiency and overlooked security tasks that often become vulnerabilities. Unsurprisingly, 90% of CISOs identify burnout as one of the main barriers that stand in the way of their teams getting more accomplished and using the full extent of their skills. How bad is burnout across cybersecurity and SOC teams? The majority of CISOs, 65%, say that burnout is a severe impediment to maintaining effective security operations. Forrester adds that 36% of the cybersecurity workforce are categorized as “Tired Rockstars,” or individuals who remain highly engaged but are on the brink of burnout. This emphasizes the critical need to address mental health and workload management proactively. SOC analysts endure heavy workloads that often turn severe when they have to monitor, analyze and aggregate insights from an average of over 10,000+ alerts a day. Chronic stress and not having enough control over their jobs lead to high turnover, with 65% considering leaving their careers. Ivanti’s 2024 Digital Employee Experience (DEX) Report underscores a vital cybersecurity link, noting that 93% of professionals agree improved DEX strengthens security, yet just 13% prioritize it. Ivanti SVP Daren Goeson told VentureBeat in a recent interview that “organizations often lack effective tools to measure digital employee experience, significantly slowing security and productivity initiatives.” SOC teams are particularly hard hit by burnout. While AI can’t solve the entire challenge, it can help automate SOC workflows and accelerate triage. Forrester is urging CISOs to think beyond automating existing processes and move forward with rationalizing security controls, deploying gen AI within existing platforms. Jeff Pollard, VP at Forrester, writes: “The only way to deal with the volatility your organization encounters is to simplify your control stack while identifying unnecessary duplicate spend and gen AI can boost productivity, but negotiating its pricing strategically will help you achieve more with less.” There are over 16 vendors of new-gen AI-based apps aimed at helping SOC teams that are in a race against time every day, especially when it comes to containing breakout times. CrowdStrike’s recent global threat report emphasizes why SOCs need to always have their A-game, as adversaries now break out within 2 minutes and 7 seconds after gaining initial access. Their recent introduction of Charlotte AI Detection Triage has proven capable of automating alert assessment with over 98% accuracy. It cuts manual triage by more than 40 hours per week, all without losing control or precision. SOCs increasingly lean on AI copilots to fight signal overload and staffing shortfalls. VentureBeat’s Security Copilot Guide (Google Sheet) provides a complete matrix with 16 vendors’ AI security copilots. What needs to be on every CISO’s roadmap in 2025 Cybersecurity leaders and their teams have significant influence on how, when and what gen AI applications and platforms their enterprises invest in. Gartner’s Phillip Shattan writes that “when it comes to generation AI-related decisions, SRM leaders wield significant influence, with over 70% reporting that cybersecurity has some influence over the decisions they make.” With so much influence on the future of gen AI investment in their organizations, CISOs need to have a solid framework or roadmap against which to plan. VentureBeat is seeing more roadmaps comparable to the one structured below for ensuring the integration of gen AI, cybersecurity and risk management initiatives. The following is a guideline that needs to be tailored to the unique needs of a business: Days 0–30: Establish core cybersecurity foundations 1. Set the goal of defining the structure and role of an AI governance framework Define formal AI policies outlining responsible data use, model training protocols, privacy controls and ethical standards. Vendors to consider: IBM AI Governance, Microsoft Purview, ServiceNow AI Governance, AWS AI Service Cards If not already in place, deploy real-time AI monitoring tools to detect unauthorized usage, anomalous behaviors and data leakage from models. Recommended platforms: Robust Intelligence, CalypsoAI, HiddenLayer, Arize AI, Credo AI, Arthur AI Train SOC, security and risk management teams on the AI-specific risks to alleviate any conflicts over how AI governance frameworks are designed to work. 2. If not already in place, get a solid Identity and Access Management (IAM) platform in place Keep building a business case for zero trust by illustrating how improving identity protection helps protect and grow revenue. Deploy a robust IAM solution to reinforce identity protection and revenue security. Top IAM platforms: If not already done, immediately conduct comprehensive audits of all user identities, focusing particularly on privileged access accounts. Enable real-time monitoring for all privileged access accounts and delete unused accounts for contractors. Implement strict least-privilege access policies, multi-factor authentication (MFA) and continuous adaptive authentication based on contextual risk assessments to strengthen your zero-trust framework. Leading Zero-Trust solutions include CrowdStrike Falcon Identity Protection, Zscaler Zero Trust Exchange, Palo Alto Networks Prisma Access, Cisco Duo Security and Cloudflare Zero Trust. Establish real-time monitoring and behavioral analytics to identify and reduce insider threats rapidly. Insider threat detection leaders: Proofpoint Insider Threat Management, Varonis DatAdvantage, Forcepoint Insider Threat, DTEX Systems, Microsoft Purview Insider Risk Management. Days 31–60: Accelerate Proactive Security Operations 1. Replace manual patch workflows with an automated patch management systems Your organization needs to move beyond fire drills and severity-based patch cycles to a continuous, real-time vulnerability monitoring and patch deployment strategy. AI is helping cut the risks of breaches with patch management. Six in ten breaches are linked to unpatched vulnerabilities. The majority of IT leaders responding to a Ponemon Institute survey, 60%, say that one or more of the breaches potentially occurred because a patch was available for a known vulnerability but not applied in time. Leading automated patch management vendors: Ivanti Neurons for Patch Management, Qualys Patch Management, Tanium Patch Management, CrowdStrike Falcon Spotlight, Rapid7 InsightVM. Implement automated tools prioritizing patches based on active exploitation, threat intelligence insights and business-critical asset prioritization. Establish transparent processes for immediate response to emerging threats, drastically reducing exposure windows. 2. Initiate comprehensive Cyber Risk Quantification (CRQ) If not already in progress in your organization, start evaluating the value of CRQ frameworks in improving how cybersecurity risks are measured and communicated in financial and business impact terms. Trusted CRQ solutions: BitSight, SecurityScorecard, Axio360, RiskLens, MetricStream, Safe Security, IBM Security Risk Quantification Services. Test out a CRQ by creating a detailed risk dashboard for executives and stakeholders, linking cybersecurity investments directly to strategic business outcomes. Conduct regular CRQ assessments to inform proactive security spending and resource allocation decisions clearly and strategically. Days 61–90: Keep optimizing security efficiency to fuel greater team resilience 1. Consolidate and Integrate Security Tools Audit existing cybersecurity tools, eliminating redundancies and streamlining capabilities into fewer, fully integrated platforms. Comprehensive integrated platforms: Palo Alto Networks Cortex XDR, Microsoft Sentinel, CrowdStrike Falcon Platform, Splunk Security Cloud, Cisco SecureX, Trellix XDR, Arctic Wolf Security Operations Cloud. Check for strong interoperability and reliable integration among cybersecurity tools to improve threat detection, response times and overall operational efficiency. Regularly review and adjust consolidated toolsets based on evolving threat landscapes and organizational security needs. 2. Implement structured burnout mitigation and automation Starting in the SOC, leverage AI-driven automation to offload repetitive cybersecurity tasks, including triage, log analysis, vulnerability scanning and initial threat triage, significantly reducing manual workloads. Recommended SOC automation tools: CrowdStrike Falcon Fusion, SentinelOne Singularity XDR, Microsoft Defender & Copilot, Palo Alto Networks Cortex XSOAR, Ivanti Neurons for Security Operations Establish structured recovery protocols, mandating cooldown periods and rotation schedules after major cybersecurity incidents to reduce analyst fatigue. Define a balanced, regular cadence of ongoing cybersecurity training, mental well-being initiatives, and institutionalized burnout mitigation practices to sustain long-term team resilience and efficiency. Automation and burnout mitigation vendors: Tines, Torq.io, Swimlane, Chronicle Security Operations Suite (Google Cloud), LogicHub SOAR+, Palo Alto Networks Cortex XSOAR Conclusion With modest budget and headcount increases, CISOs and their teams are being called to defend more threat vectors than ever. Many tell VentureBeat it’s a continual balancing act that demands more time, training, and trade-offs on which legacy apps stay and which go, all defining how their future tech stack will look. CISOs who see gen AI as a strategic technology that can help unify and close gaps in security infrastructure are thorough in their vetting of new apps and tools before they go into production. While gen AI continues to fuel new adversarial AI techniques and tradecraft, cybersecurity vendors respond by accelerating the development of next-generation products. Paradoxically, the more advanced threatcraft becomes with adversarial AI, the more critical it becomes for defenders adopting AI to pursue and perfect human-in-the-middle designs that can flex and adapt to changing threats. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

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2025-05-14 05:39:21 ·

What your tools miss at 2:13 AM: How gen AI attack chains exploit telemetry lag – Part 2

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage.
Learn More
Generative AI is creating a digital diaspora of techniques, technologies and tradecraft that everyone, from rogue attackers to nation-state cyber armies trained in the art of cyberwar, is adopting.
Insider threats are growing, too, accelerated by job insecurity and growing inflation.
All these challenges and more fall on the shoulders of the CISO, and it’s no wonder more are dealing with burnout.
In Part 1:
We explored how gen AI is reshaping the threat landscape, accelerating insider threats and putting unprecedented pressure on cybersecurity teams.
Insider-driven risks, shadow AI usage and outdated detection models are forcing CISOs to rethink their defenses.Now, in Part 2, we turn to the solutions — how gen AI can help combat burnout across security operations centers (SOCs), enable smarter automation and guide CISOs through a 90-day roadmap to secure their enterprises against evolving threats.
Battling burnout with gen AI deserves to be a 2025 CISO priority
Nearly one in four CISOs consider quitting, with 93% citing extreme stress, further proving that burnout is creating increasingly severe operational and human risks.
Gartner’s most recent research links burnout to decreased team efficiency and overlooked security tasks that often become vulnerabilities.
Unsurprisingly, 90% of CISOs identify burnout as one of the main barriers that stand in the way of their teams getting more accomplished and using the full extent of their skills.
How bad is burnout across cybersecurity and SOC teams? The majority of CISOs, 65%, say that burnout is a severe impediment to maintaining effective security operations.
Forrester adds that 36% of the cybersecurity workforce are categorized as “Tired Rockstars,” or individuals who remain highly engaged but are on the brink of burnout.
This emphasizes the critical need to address mental health and workload management proactively.
SOC analysts endure heavy workloads that often turn severe when they have to monitor, analyze and aggregate insights from an average of over 10,000+ alerts a day.
Chronic stress and not having enough control over their jobs lead to high turnover, with 65% considering leaving their careers.
Ivanti’s 2024 Digital Employee Experience (DEX) Report underscores a vital cybersecurity link, noting that 93% of professionals agree improved DEX strengthens security, yet just 13% prioritize it.
Ivanti SVP Daren Goeson told VentureBeat in a recent interview that “organizations often lack effective tools to measure digital employee experience, significantly slowing security and productivity initiatives.”
SOC teams are particularly hard hit by burnout.
While AI can’t solve the entire challenge, it can help automate SOC workflows and accelerate triage.
Forrester is urging CISOs to think beyond automating existing processes and move forward with rationalizing security controls, deploying gen AI within existing platforms.
Jeff Pollard, VP at Forrester, writes: “The only way to deal with the volatility your organization encounters is to simplify your control stack while identifying unnecessary duplicate spend and gen AI can boost productivity, but negotiating its pricing strategically will help you achieve more with less.”
There are over 16 vendors of new-gen AI-based apps aimed at helping SOC teams that are in a race against time every day, especially when it comes to containing breakout times.
CrowdStrike’s recent global threat report emphasizes why SOCs need to always have their A-game, as adversaries now break out within 2 minutes and 7 seconds after gaining initial access.
Their recent introduction of Charlotte AI Detection Triage has proven capable of automating alert assessment with over 98% accuracy.
It cuts manual triage by more than 40 hours per week, all without losing control or precision.
SOCs increasingly lean on AI copilots to fight signal overload and staffing shortfalls.
VentureBeat’s Security Copilot Guide (Google Sheet) provides a complete matrix with 16 vendors’ AI security copilots.
What needs to be on every CISO’s roadmap in 2025
Cybersecurity leaders and their teams have significant influence on how, when and what gen AI applications and platforms their enterprises invest in.
Gartner’s Phillip Shattan writes that “when it comes to generation AI-related decisions, SRM leaders wield significant influence, with over 70% reporting that cybersecurity has some influence over the decisions they make.”
With so much influence on the future of gen AI investment in their organizations, CISOs need to have a solid framework or roadmap against which to plan.
VentureBeat is seeing more roadmaps comparable to the one structured below for ensuring the integration of gen AI, cybersecurity and risk management initiatives.
The following is a guideline that needs to be tailored to the unique needs of a business:
Days 0–30: Establish core cybersecurity foundations
1.
Set the goal of defining the structure and role of an AI governance framework
Define formal AI policies outlining responsible data use, model training protocols, privacy controls and ethical standards.

Vendors to consider: IBM AI Governance, Microsoft Purview, ServiceNow AI Governance, AWS AI Service Cards
If not already in place, deploy real-time AI monitoring tools to detect unauthorized usage, anomalous behaviors and data leakage from models.

Recommended platforms: Robust Intelligence, CalypsoAI, HiddenLayer, Arize AI, Credo AI, Arthur AI
Train SOC, security and risk management teams on the AI-specific risks to alleviate any conflicts over how AI governance frameworks are designed to work.

2.
If not already in place, get a solid Identity and Access Management (IAM) platform in place
Keep building a business case for zero trust by illustrating how improving identity protection helps protect and grow revenue.

Deploy a robust IAM solution to reinforce identity protection and revenue security.

Top IAM platforms:
If not already done, immediately conduct comprehensive audits of all user identities, focusing particularly on privileged access accounts.
Enable real-time monitoring for all privileged access accounts and delete unused accounts for contractors.
Implement strict least-privilege access policies, multi-factor authentication (MFA) and continuous adaptive authentication based on contextual risk assessments to strengthen your zero-trust framework.

Leading Zero-Trust solutions include CrowdStrike Falcon Identity Protection, Zscaler Zero Trust Exchange, Palo Alto Networks Prisma Access, Cisco Duo Security and Cloudflare Zero Trust.
Establish real-time monitoring and behavioral analytics to identify and reduce insider threats rapidly.
Insider threat detection leaders: Proofpoint Insider Threat Management, Varonis DatAdvantage, Forcepoint Insider Threat, DTEX Systems, Microsoft Purview Insider Risk Management.
Days 31–60: Accelerate Proactive Security Operations
1.
Replace manual patch workflows with an automated patch management systems
Your organization needs to move beyond fire drills and severity-based patch cycles to a continuous, real-time vulnerability monitoring and patch deployment strategy.
AI is helping cut the risks of breaches with patch management.
Six in ten breaches are linked to unpatched vulnerabilities.
The majority of IT leaders responding to a Ponemon Institute survey, 60%, say that one or more of the breaches potentially occurred because a patch was available for a known vulnerability but not applied in time.

Leading automated patch management vendors: Ivanti Neurons for Patch Management, Qualys Patch Management, Tanium Patch Management, CrowdStrike Falcon Spotlight, Rapid7 InsightVM.
Implement automated tools prioritizing patches based on active exploitation, threat intelligence insights and business-critical asset prioritization.
Establish transparent processes for immediate response to emerging threats, drastically reducing exposure windows.
2.
Initiate comprehensive Cyber Risk Quantification (CRQ)
If not already in progress in your organization, start evaluating the value of CRQ frameworks in improving how cybersecurity risks are measured and communicated in financial and business impact terms.
Trusted CRQ solutions: BitSight, SecurityScorecard, Axio360, RiskLens, MetricStream, Safe Security, IBM Security Risk Quantification Services.
Test out a CRQ by creating a detailed risk dashboard for executives and stakeholders, linking cybersecurity investments directly to strategic business outcomes.
Conduct regular CRQ assessments to inform proactive security spending and resource allocation decisions clearly and strategically.
Days 61–90: Keep optimizing security efficiency to fuel greater team resilience
1.
Consolidate and Integrate Security Tools
Audit existing cybersecurity tools, eliminating redundancies and streamlining capabilities into fewer, fully integrated platforms.

Comprehensive integrated platforms: Palo Alto Networks Cortex XDR, Microsoft Sentinel, CrowdStrike Falcon Platform, Splunk Security Cloud, Cisco SecureX, Trellix XDR, Arctic Wolf Security Operations Cloud.
Check for strong interoperability and reliable integration among cybersecurity tools to improve threat detection, response times and overall operational efficiency.
Regularly review and adjust consolidated toolsets based on evolving threat landscapes and organizational security needs.
2.
Implement structured burnout mitigation and automation
Starting in the SOC, leverage AI-driven automation to offload repetitive cybersecurity tasks, including triage, log analysis, vulnerability scanning and initial threat triage, significantly reducing manual workloads.

Recommended SOC automation tools: CrowdStrike Falcon Fusion, SentinelOne Singularity XDR, Microsoft Defender & Copilot, Palo Alto Networks Cortex XSOAR, Ivanti Neurons for Security Operations
Establish structured recovery protocols, mandating cooldown periods and rotation schedules after major cybersecurity incidents to reduce analyst fatigue.
Define a balanced, regular cadence of ongoing cybersecurity training, mental well-being initiatives, and institutionalized burnout mitigation practices to sustain long-term team resilience and efficiency.

Automation and burnout mitigation vendors: Tines, Torq.io, Swimlane, Chronicle Security Operations Suite (Google Cloud), LogicHub SOAR+, Palo Alto Networks Cortex XSOAR
Conclusion
With modest budget and headcount increases, CISOs and their teams are being called to defend more threat vectors than ever.
Many tell VentureBeat it’s a continual balancing act that demands more time, training, and trade-offs on which legacy apps stay and which go, all defining how their future tech stack will look.
CISOs who see gen AI as a strategic technology that can help unify and close gaps in security infrastructure are thorough in their vetting of new apps and tools before they go into production.
While gen AI continues to fuel new adversarial AI techniques and tradecraft, cybersecurity vendors respond by accelerating the development of next-generation products.
Paradoxically, the more advanced threatcraft becomes with adversarial AI, the more critical it becomes for defenders adopting AI to pursue and perfect human-in-the-middle designs that can flex and adapt to changing threats.
Daily insights on business use cases with VB Daily
If you want to impress your boss, VB Daily has you covered.
We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.
Read our Privacy Policy
Thanks for subscribing.
Check out more VB newsletters here.

An error occured.

Source: https://venturebeat.com/ai/what-your-tools-miss-at-213-am-how-gen-ai-attack-chains-exploit-telemetry-lag-part-2/" style="color: #0066cc;">https://venturebeat.com/ai/what-your-tools-miss-at-213-am-how-gen-ai-attack-chains-exploit-telemetry-lag-part-2/
#what #your #tools #miss #how #gen #attack #chains #exploit #telemetry #lag #part

What your tools miss at 2:13 AM: How gen AI attack chains exploit telemetry lag – Part 2
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Generative AI is creating a digital diaspora of techniques, technologies and tradecraft that everyone, from rogue attackers to nation-state cyber armies trained in the art of cyberwar, is adopting. Insider threats are growing, too, accelerated by job insecurity and growing inflation. All these challenges and more fall on the shoulders of the CISO, and it’s no wonder more are dealing with burnout. In Part 1: We explored how gen AI is reshaping the threat landscape, accelerating insider threats and putting unprecedented pressure on cybersecurity teams. Insider-driven risks, shadow AI usage and outdated detection models are forcing CISOs to rethink their defenses.Now, in Part 2, we turn to the solutions — how gen AI can help combat burnout across security operations centers (SOCs), enable smarter automation and guide CISOs through a 90-day roadmap to secure their enterprises against evolving threats. Battling burnout with gen AI deserves to be a 2025 CISO priority Nearly one in four CISOs consider quitting, with 93% citing extreme stress, further proving that burnout is creating increasingly severe operational and human risks. Gartner’s most recent research links burnout to decreased team efficiency and overlooked security tasks that often become vulnerabilities. Unsurprisingly, 90% of CISOs identify burnout as one of the main barriers that stand in the way of their teams getting more accomplished and using the full extent of their skills. How bad is burnout across cybersecurity and SOC teams? The majority of CISOs, 65%, say that burnout is a severe impediment to maintaining effective security operations. Forrester adds that 36% of the cybersecurity workforce are categorized as “Tired Rockstars,” or individuals who remain highly engaged but are on the brink of burnout. This emphasizes the critical need to address mental health and workload management proactively. SOC analysts endure heavy workloads that often turn severe when they have to monitor, analyze and aggregate insights from an average of over 10,000+ alerts a day. Chronic stress and not having enough control over their jobs lead to high turnover, with 65% considering leaving their careers. Ivanti’s 2024 Digital Employee Experience (DEX) Report underscores a vital cybersecurity link, noting that 93% of professionals agree improved DEX strengthens security, yet just 13% prioritize it. Ivanti SVP Daren Goeson told VentureBeat in a recent interview that “organizations often lack effective tools to measure digital employee experience, significantly slowing security and productivity initiatives.” SOC teams are particularly hard hit by burnout. While AI can’t solve the entire challenge, it can help automate SOC workflows and accelerate triage. Forrester is urging CISOs to think beyond automating existing processes and move forward with rationalizing security controls, deploying gen AI within existing platforms. Jeff Pollard, VP at Forrester, writes: “The only way to deal with the volatility your organization encounters is to simplify your control stack while identifying unnecessary duplicate spend and gen AI can boost productivity, but negotiating its pricing strategically will help you achieve more with less.” There are over 16 vendors of new-gen AI-based apps aimed at helping SOC teams that are in a race against time every day, especially when it comes to containing breakout times. CrowdStrike’s recent global threat report emphasizes why SOCs need to always have their A-game, as adversaries now break out within 2 minutes and 7 seconds after gaining initial access. Their recent introduction of Charlotte AI Detection Triage has proven capable of automating alert assessment with over 98% accuracy. It cuts manual triage by more than 40 hours per week, all without losing control or precision. SOCs increasingly lean on AI copilots to fight signal overload and staffing shortfalls. VentureBeat’s Security Copilot Guide (Google Sheet) provides a complete matrix with 16 vendors’ AI security copilots. What needs to be on every CISO’s roadmap in 2025 Cybersecurity leaders and their teams have significant influence on how, when and what gen AI applications and platforms their enterprises invest in. Gartner’s Phillip Shattan writes that “when it comes to generation AI-related decisions, SRM leaders wield significant influence, with over 70% reporting that cybersecurity has some influence over the decisions they make.” With so much influence on the future of gen AI investment in their organizations, CISOs need to have a solid framework or roadmap against which to plan. VentureBeat is seeing more roadmaps comparable to the one structured below for ensuring the integration of gen AI, cybersecurity and risk management initiatives. The following is a guideline that needs to be tailored to the unique needs of a business: Days 0–30: Establish core cybersecurity foundations 1. Set the goal of defining the structure and role of an AI governance framework Define formal AI policies outlining responsible data use, model training protocols, privacy controls and ethical standards. Vendors to consider: IBM AI Governance, Microsoft Purview, ServiceNow AI Governance, AWS AI Service Cards If not already in place, deploy real-time AI monitoring tools to detect unauthorized usage, anomalous behaviors and data leakage from models. Recommended platforms: Robust Intelligence, CalypsoAI, HiddenLayer, Arize AI, Credo AI, Arthur AI Train SOC, security and risk management teams on the AI-specific risks to alleviate any conflicts over how AI governance frameworks are designed to work. 2. If not already in place, get a solid Identity and Access Management (IAM) platform in place Keep building a business case for zero trust by illustrating how improving identity protection helps protect and grow revenue. Deploy a robust IAM solution to reinforce identity protection and revenue security. Top IAM platforms: If not already done, immediately conduct comprehensive audits of all user identities, focusing particularly on privileged access accounts. Enable real-time monitoring for all privileged access accounts and delete unused accounts for contractors. Implement strict least-privilege access policies, multi-factor authentication (MFA) and continuous adaptive authentication based on contextual risk assessments to strengthen your zero-trust framework. Leading Zero-Trust solutions include CrowdStrike Falcon Identity Protection, Zscaler Zero Trust Exchange, Palo Alto Networks Prisma Access, Cisco Duo Security and Cloudflare Zero Trust. Establish real-time monitoring and behavioral analytics to identify and reduce insider threats rapidly. Insider threat detection leaders: Proofpoint Insider Threat Management, Varonis DatAdvantage, Forcepoint Insider Threat, DTEX Systems, Microsoft Purview Insider Risk Management. Days 31–60: Accelerate Proactive Security Operations 1. Replace manual patch workflows with an automated patch management systems Your organization needs to move beyond fire drills and severity-based patch cycles to a continuous, real-time vulnerability monitoring and patch deployment strategy. AI is helping cut the risks of breaches with patch management. Six in ten breaches are linked to unpatched vulnerabilities. The majority of IT leaders responding to a Ponemon Institute survey, 60%, say that one or more of the breaches potentially occurred because a patch was available for a known vulnerability but not applied in time. Leading automated patch management vendors: Ivanti Neurons for Patch Management, Qualys Patch Management, Tanium Patch Management, CrowdStrike Falcon Spotlight, Rapid7 InsightVM. Implement automated tools prioritizing patches based on active exploitation, threat intelligence insights and business-critical asset prioritization. Establish transparent processes for immediate response to emerging threats, drastically reducing exposure windows. 2. Initiate comprehensive Cyber Risk Quantification (CRQ) If not already in progress in your organization, start evaluating the value of CRQ frameworks in improving how cybersecurity risks are measured and communicated in financial and business impact terms. Trusted CRQ solutions: BitSight, SecurityScorecard, Axio360, RiskLens, MetricStream, Safe Security, IBM Security Risk Quantification Services. Test out a CRQ by creating a detailed risk dashboard for executives and stakeholders, linking cybersecurity investments directly to strategic business outcomes. Conduct regular CRQ assessments to inform proactive security spending and resource allocation decisions clearly and strategically. Days 61–90: Keep optimizing security efficiency to fuel greater team resilience 1. Consolidate and Integrate Security Tools Audit existing cybersecurity tools, eliminating redundancies and streamlining capabilities into fewer, fully integrated platforms. Comprehensive integrated platforms: Palo Alto Networks Cortex XDR, Microsoft Sentinel, CrowdStrike Falcon Platform, Splunk Security Cloud, Cisco SecureX, Trellix XDR, Arctic Wolf Security Operations Cloud. Check for strong interoperability and reliable integration among cybersecurity tools to improve threat detection, response times and overall operational efficiency. Regularly review and adjust consolidated toolsets based on evolving threat landscapes and organizational security needs. 2. Implement structured burnout mitigation and automation Starting in the SOC, leverage AI-driven automation to offload repetitive cybersecurity tasks, including triage, log analysis, vulnerability scanning and initial threat triage, significantly reducing manual workloads. Recommended SOC automation tools: CrowdStrike Falcon Fusion, SentinelOne Singularity XDR, Microsoft Defender & Copilot, Palo Alto Networks Cortex XSOAR, Ivanti Neurons for Security Operations Establish structured recovery protocols, mandating cooldown periods and rotation schedules after major cybersecurity incidents to reduce analyst fatigue. Define a balanced, regular cadence of ongoing cybersecurity training, mental well-being initiatives, and institutionalized burnout mitigation practices to sustain long-term team resilience and efficiency. Automation and burnout mitigation vendors: Tines, Torq.io, Swimlane, Chronicle Security Operations Suite (Google Cloud), LogicHub SOAR+, Palo Alto Networks Cortex XSOAR Conclusion With modest budget and headcount increases, CISOs and their teams are being called to defend more threat vectors than ever. Many tell VentureBeat it’s a continual balancing act that demands more time, training, and trade-offs on which legacy apps stay and which go, all defining how their future tech stack will look. CISOs who see gen AI as a strategic technology that can help unify and close gaps in security infrastructure are thorough in their vetting of new apps and tools before they go into production. While gen AI continues to fuel new adversarial AI techniques and tradecraft, cybersecurity vendors respond by accelerating the development of next-generation products. Paradoxically, the more advanced threatcraft becomes with adversarial AI, the more critical it becomes for defenders adopting AI to pursue and perfect human-in-the-middle designs that can flex and adapt to changing threats. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured. Source: https://venturebeat.com/ai/what-your-tools-miss-at-213-am-how-gen-ai-attack-chains-exploit-telemetry-lag-part-2/ #what #your #tools #miss #how #gen #attack #chains #exploit #telemetry #lag #part

VENTUREBEAT.COM

What your tools miss at 2:13 AM: How gen AI attack chains exploit telemetry lag – Part 2

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Generative AI is creating a digital diaspora of techniques, technologies and tradecraft that everyone, from rogue attackers to nation-state cyber armies trained in the art of cyberwar, is adopting. Insider threats are growing, too, accelerated by job insecurity and growing inflation. All these challenges and more fall on the shoulders of the CISO, and it’s no wonder more are dealing with burnout. In Part 1: We explored how gen AI is reshaping the threat landscape, accelerating insider threats and putting unprecedented pressure on cybersecurity teams. Insider-driven risks, shadow AI usage and outdated detection models are forcing CISOs to rethink their defenses.Now, in Part 2, we turn to the solutions — how gen AI can help combat burnout across security operations centers (SOCs), enable smarter automation and guide CISOs through a 90-day roadmap to secure their enterprises against evolving threats. Battling burnout with gen AI deserves to be a 2025 CISO priority Nearly one in four CISOs consider quitting, with 93% citing extreme stress, further proving that burnout is creating increasingly severe operational and human risks. Gartner’s most recent research links burnout to decreased team efficiency and overlooked security tasks that often become vulnerabilities. Unsurprisingly, 90% of CISOs identify burnout as one of the main barriers that stand in the way of their teams getting more accomplished and using the full extent of their skills. How bad is burnout across cybersecurity and SOC teams? The majority of CISOs, 65%, say that burnout is a severe impediment to maintaining effective security operations. Forrester adds that 36% of the cybersecurity workforce are categorized as “Tired Rockstars,” or individuals who remain highly engaged but are on the brink of burnout. This emphasizes the critical need to address mental health and workload management proactively. SOC analysts endure heavy workloads that often turn severe when they have to monitor, analyze and aggregate insights from an average of over 10,000+ alerts a day. Chronic stress and not having enough control over their jobs lead to high turnover, with 65% considering leaving their careers. Ivanti’s 2024 Digital Employee Experience (DEX) Report underscores a vital cybersecurity link, noting that 93% of professionals agree improved DEX strengthens security, yet just 13% prioritize it. Ivanti SVP Daren Goeson told VentureBeat in a recent interview that “organizations often lack effective tools to measure digital employee experience, significantly slowing security and productivity initiatives.” SOC teams are particularly hard hit by burnout. While AI can’t solve the entire challenge, it can help automate SOC workflows and accelerate triage. Forrester is urging CISOs to think beyond automating existing processes and move forward with rationalizing security controls, deploying gen AI within existing platforms. Jeff Pollard, VP at Forrester, writes: “The only way to deal with the volatility your organization encounters is to simplify your control stack while identifying unnecessary duplicate spend and gen AI can boost productivity, but negotiating its pricing strategically will help you achieve more with less.” There are over 16 vendors of new-gen AI-based apps aimed at helping SOC teams that are in a race against time every day, especially when it comes to containing breakout times. CrowdStrike’s recent global threat report emphasizes why SOCs need to always have their A-game, as adversaries now break out within 2 minutes and 7 seconds after gaining initial access. Their recent introduction of Charlotte AI Detection Triage has proven capable of automating alert assessment with over 98% accuracy. It cuts manual triage by more than 40 hours per week, all without losing control or precision. SOCs increasingly lean on AI copilots to fight signal overload and staffing shortfalls. VentureBeat’s Security Copilot Guide (Google Sheet) provides a complete matrix with 16 vendors’ AI security copilots. What needs to be on every CISO’s roadmap in 2025 Cybersecurity leaders and their teams have significant influence on how, when and what gen AI applications and platforms their enterprises invest in. Gartner’s Phillip Shattan writes that “when it comes to generation AI-related decisions, SRM leaders wield significant influence, with over 70% reporting that cybersecurity has some influence over the decisions they make.” With so much influence on the future of gen AI investment in their organizations, CISOs need to have a solid framework or roadmap against which to plan. VentureBeat is seeing more roadmaps comparable to the one structured below for ensuring the integration of gen AI, cybersecurity and risk management initiatives. The following is a guideline that needs to be tailored to the unique needs of a business: Days 0–30: Establish core cybersecurity foundations 1. Set the goal of defining the structure and role of an AI governance framework Define formal AI policies outlining responsible data use, model training protocols, privacy controls and ethical standards. Vendors to consider: IBM AI Governance, Microsoft Purview, ServiceNow AI Governance, AWS AI Service Cards If not already in place, deploy real-time AI monitoring tools to detect unauthorized usage, anomalous behaviors and data leakage from models. Recommended platforms: Robust Intelligence, CalypsoAI, HiddenLayer, Arize AI, Credo AI, Arthur AI Train SOC, security and risk management teams on the AI-specific risks to alleviate any conflicts over how AI governance frameworks are designed to work. 2. If not already in place, get a solid Identity and Access Management (IAM) platform in place Keep building a business case for zero trust by illustrating how improving identity protection helps protect and grow revenue. Deploy a robust IAM solution to reinforce identity protection and revenue security. Top IAM platforms: If not already done, immediately conduct comprehensive audits of all user identities, focusing particularly on privileged access accounts. Enable real-time monitoring for all privileged access accounts and delete unused accounts for contractors. Implement strict least-privilege access policies, multi-factor authentication (MFA) and continuous adaptive authentication based on contextual risk assessments to strengthen your zero-trust framework. Leading Zero-Trust solutions include CrowdStrike Falcon Identity Protection, Zscaler Zero Trust Exchange, Palo Alto Networks Prisma Access, Cisco Duo Security and Cloudflare Zero Trust. Establish real-time monitoring and behavioral analytics to identify and reduce insider threats rapidly. Insider threat detection leaders: Proofpoint Insider Threat Management, Varonis DatAdvantage, Forcepoint Insider Threat, DTEX Systems, Microsoft Purview Insider Risk Management. Days 31–60: Accelerate Proactive Security Operations 1. Replace manual patch workflows with an automated patch management systems Your organization needs to move beyond fire drills and severity-based patch cycles to a continuous, real-time vulnerability monitoring and patch deployment strategy. AI is helping cut the risks of breaches with patch management. Six in ten breaches are linked to unpatched vulnerabilities. The majority of IT leaders responding to a Ponemon Institute survey, 60%, say that one or more of the breaches potentially occurred because a patch was available for a known vulnerability but not applied in time. Leading automated patch management vendors: Ivanti Neurons for Patch Management, Qualys Patch Management, Tanium Patch Management, CrowdStrike Falcon Spotlight, Rapid7 InsightVM. Implement automated tools prioritizing patches based on active exploitation, threat intelligence insights and business-critical asset prioritization. Establish transparent processes for immediate response to emerging threats, drastically reducing exposure windows. 2. Initiate comprehensive Cyber Risk Quantification (CRQ) If not already in progress in your organization, start evaluating the value of CRQ frameworks in improving how cybersecurity risks are measured and communicated in financial and business impact terms. Trusted CRQ solutions: BitSight, SecurityScorecard, Axio360, RiskLens, MetricStream, Safe Security, IBM Security Risk Quantification Services. Test out a CRQ by creating a detailed risk dashboard for executives and stakeholders, linking cybersecurity investments directly to strategic business outcomes. Conduct regular CRQ assessments to inform proactive security spending and resource allocation decisions clearly and strategically. Days 61–90: Keep optimizing security efficiency to fuel greater team resilience 1. Consolidate and Integrate Security Tools Audit existing cybersecurity tools, eliminating redundancies and streamlining capabilities into fewer, fully integrated platforms. Comprehensive integrated platforms: Palo Alto Networks Cortex XDR, Microsoft Sentinel, CrowdStrike Falcon Platform, Splunk Security Cloud, Cisco SecureX, Trellix XDR, Arctic Wolf Security Operations Cloud. Check for strong interoperability and reliable integration among cybersecurity tools to improve threat detection, response times and overall operational efficiency. Regularly review and adjust consolidated toolsets based on evolving threat landscapes and organizational security needs. 2. Implement structured burnout mitigation and automation Starting in the SOC, leverage AI-driven automation to offload repetitive cybersecurity tasks, including triage, log analysis, vulnerability scanning and initial threat triage, significantly reducing manual workloads. Recommended SOC automation tools: CrowdStrike Falcon Fusion, SentinelOne Singularity XDR, Microsoft Defender & Copilot, Palo Alto Networks Cortex XSOAR, Ivanti Neurons for Security Operations Establish structured recovery protocols, mandating cooldown periods and rotation schedules after major cybersecurity incidents to reduce analyst fatigue. Define a balanced, regular cadence of ongoing cybersecurity training, mental well-being initiatives, and institutionalized burnout mitigation practices to sustain long-term team resilience and efficiency. Automation and burnout mitigation vendors: Tines, Torq.io, Swimlane, Chronicle Security Operations Suite (Google Cloud), LogicHub SOAR+, Palo Alto Networks Cortex XSOAR Conclusion With modest budget and headcount increases, CISOs and their teams are being called to defend more threat vectors than ever. Many tell VentureBeat it’s a continual balancing act that demands more time, training, and trade-offs on which legacy apps stay and which go, all defining how their future tech stack will look. CISOs who see gen AI as a strategic technology that can help unify and close gaps in security infrastructure are thorough in their vetting of new apps and tools before they go into production. While gen AI continues to fuel new adversarial AI techniques and tradecraft, cybersecurity vendors respond by accelerating the development of next-generation products. Paradoxically, the more advanced threatcraft becomes with adversarial AI, the more critical it becomes for defenders adopting AI to pursue and perfect human-in-the-middle designs that can flex and adapt to changing threats. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

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