In my town of Bend, Oregon, where the spirit of independence and community thrives, the concept of “Democratic AI” resonates in a uniquely powerful way. In a world increasingly shaped by algorithms and artificial intelligence (AI), the notion of democratizing its creation, access, and governance offers a powerful counterpoint to the centralized control often associated with Big Tech. But what exactly is Democratic AI, and why might it be the best, perhaps even the only truly sustainable path forward for this transformative technology? Buckle up, because we’re about to dive into the digital town hall of the future. Then, we’ll close with my Product of the Week: Slate’s new pickup truck, backed by Jeff Bezos, which could transform the EV market. What Democratic AI Really Means At its core, Democratic AI is a philosophy and set of practices aimed at distributing the power of AI more broadly. It encompasses several key principles: Openness and Transparency: The underlying code, data, and models are often open-source or readily accessible for scrutiny and modification. Think of it as the difference between a proprietary black box and a transparent, well-documented library. Decentralization of Development: Instead of being solely the domain of large corporations with vast resources, Democratic AI encourages contributions from a diverse range of individuals, researchers, smaller organizations, and even governments. It’s the digital equivalent of a community barn-raising. Participatory Governance: The ethical guidelines, development priorities, and deployment strategies are shaped through broader stakeholder involvement rather than top-down mandates. Imagine citizens having a say in how AI is used in their communities. Accessibility and Affordability: The tools and resources needed to develop and utilize AI are made as widely available and affordable as possible, breaking down barriers to entry. It’s about leveling the playing field so that innovation isn’t limited by deep pockets. Data Sovereignty and Privacy: Individuals and communities retain greater control over their data, and privacy is prioritized when developing and deploying AI systems. It’s about ensuring AI serves people, not the other way around. Democratic AI May Be the Best Path So, why is this open and collaborative approach potentially superior to more traditional, often proprietary, AI models? Here are several advantages it brings to the table: Faster and More Diverse Innovation: When you open the floodgates to contributions from a global community, the pace of innovation explodes. Diverse perspectives and skill sets lead to more creative solutions and the exploration of a wider range of ideas, outpacing what any single organization could accomplish. It’s like having a thousand brilliant minds tackling a problem instead of just a handful. Increased Trust and Accountability: Transparency in code and data allows for greater scrutiny, making it easier to identify and address biases, errors, and potential security vulnerabilities. When the workings of an AI are open for all to see, there’s a greater sense of trust and accountability. It’s harder to hide digital shenanigans in broad daylight. Reduced Vendor Lock-In and Monopoly Risk: By promoting open standards and interoperability, Democratic AI reduces reliance on proprietary platforms, fostering a more competitive landscape and mitigating the risks associated with the dominance of a few powerful AI providers. It’s about avoiding digital monopolies where a few companies control core AI resources. Alignment with Public Good: With broader participation in governance and ethical considerations, Democratic AI is more likely to be aligned with the public good and societal values rather than solely driven by corporate profits or narrow interests. It’s about building AI that serves humanity, not just shareholders. Empowerment and Skill Development: Democratizing AI empowers individuals and smaller organizations to become creators and innovators, fostering a broader understanding of the technology and driving the development of local expertise. It’s about turning passive consumers into active participants in the AI revolution. Governments and Companies Advancing Democratic AI While the concept is still evolving, several governments and companies are dipping their toes, or even diving headfirst, into the waters of Democratic AI: The European Union: With its emphasis on digital sovereignty and open-source initiatives, the EU actively promotes a more democratic and human-centric approach to AI development and regulation. Various Open-Source AI Initiatives: Projects like Hugging Face, with its open platform for models and datasets, and initiatives around open data for AI training, embody the spirit of Democratic AI. Decentralized AI Platforms: Emerging projects are exploring blockchain and other decentralized technologies to create more open and community-governed AI infrastructure. Government-Backed Open AI Research: Some governments are supporting open research efforts to promote collaboration and transparency in AI development. For example, Canada funds its CIFAR AI Chairs program, and the U.K. advances similar goals through the Alan Turing Institute. Benefits: A Brighter Algorithmic Future Aggressively pursuing Democratic AI could deliver transformative results: More Ethical and Fair AI Systems: Open scrutiny and diverse participation can help mitigate biases embedded in data and algorithms, leading to fairer and more equitable AI outcomes. AI Tailored to Diverse Needs: A decentralized and collaborative approach can foster the development of AI solutions that address the specific needs and contexts of diverse communities and cultures. Greater Public Trust in AI: Transparency and participatory governance can build greater public trust in AI systems, fostering wider adoption and acceptance. Accelerated Solutions to Global Challenges: By harnessing the collective intelligence of a global community, Democratic AI can accelerate the development of solutions to pressing global challenges, from climate change to health care. Where Democratic AI Stands Today The concept of Democratic AI is still in its relatively early stages. While the principles of open source have a long history in software development, applying them comprehensively to the complex world of AI — including data, models, and governance — is a more recent endeavor. We are likely in the “seed” or early “sapling” phase of this movement. While there are promising initiatives and growing awareness, widespread adoption and the establishment of robust Democratic AI ecosystems will take time, research, and concerted effort from individuals, organizations, and governments. Wrapping Up: A People-Led AI Future Democratic AI offers a compelling vision for the future of artificial intelligence, one where power is distributed, innovation is accelerated through collaboration, and the technology serves humanity’s broader interests. While the path to realizing this vision is still unfolding, the principles of openness, transparency, and participatory governance hold immense promise. As we navigate the transformative power of AI, embracing a democratic approach might not just be the best way forward; it might be the only way to ensure that this powerful technology truly benefits all of us, here in Bend and across the interconnected world. The seeds of a people’s AI are being planted, and it’s up to us to cultivate a future where everyone shares its fruits. Slate Electric Pickup – Bezos-Backed Bargain With a Twist The buzz around the Slate electric pickup truck has been palpable, and after its recent unveiling, it’s clear why. This no-nonsense EV, with ties to Amazon founder Jeff Bezos through his investment in the parent company Re:Build Manufacturing, is making waves with its incredibly aggressive starting price of just $25,000. In a market where electric trucks often flirt with six-figure sums, Slate is aiming for the heart of the value-conscious buyer. What makes the Slate EV truly intriguing is its innovative modular construction. Reportedly, the pickup can be converted into a compact SUV with a relatively inexpensive kit. This transformative capability addresses the reality of how many pickup owners actually use their vehicles. For the vast majority, the truck bed often sits empty or carries lighter loads, while the need for passenger space and enclosed cargo for family and everyday life is more frequent. Slate’s ability to morph into an SUV offers unparalleled versatility, essentially providing two vehicles in one at an exceptionally low total cost of ownership. Slate’s price point makes it especially appealing to buyers focused on practicality and value. At $25,000, it’s positioned to be one of the most affordable EVs on the market, let alone an electric truck. This price point opens electric mobility to buyers who were previously priced out of the EV market. Considering that most pickup truck owners primarily use their vehicles for commuting, errands, and occasional light hauling, Slate’s EV core functionality likely meets their needs without the excess capacity and exorbitant cost of larger, more powerful trucks. Adding to its affordability, Slate’s truck is reportedly still eligible for the U.S. federal tax credit for electric vehicles, currently as much as $7,500. For those who qualify (check with your tax advisor), this effectively brings the starting price down to a mere $17,500, making it an absolute steal and a compelling alternative to many gasoline-powered used vehicles. While specific performance figures are still emerging, early reports suggest a respectable range suitable for daily driving, a powertrain adequate for typical truck duties, and the added weight of its modular components. Its focus on affordability likely means it won’t boast the blistering acceleration of high-end EVs, but it promises a practical and efficient driving experience. The Slate electric pickup, with its Bezos connection, groundbreaking modularity, and incredibly low price, could very well be the value king of the electric truck revolution. The clear value of this Slate pickup — and the fact that it’s likely giving Elon Musk nightmares — makes it my Product of the Week. Credit: The Slate pickup truck images are courtesy of Slate.

TechTarget and Informa Tech’s Digital Business Combine.TechTarget and InformaTechTarget and Informa Tech’s Digital Business Combine.Together, we power an unparalleled network of 220+ online properties covering 10,000+ granular topics, serving an audience of 50+ million professionals with original, objective content from trusted sources. We help you gain critical insights and make more informed decisions across your business priorities.Breaking Bread: Do You Understand Your Data?Breaking Bread: Do You Understand Your Data?As IT leadership wrangles with data governance and policies, can operations glean useful information without stepping on a few toes?Joao-Pierre S. Ruth, Senior EditorMay 19, 2025What makes data actionable and real for an organization?Many enterprises capture data as part of their operations and activity but taking that information to another usable level can be a challenge. There may be security concerns, privacy regulations to adhere to, conflicting requirements among stakeholders within the organization.If the world runs on data that fuels the software that gets put into action, how do you separate the wheat from the chaff? And how do you not step on too many toes along the way?In this Breaking Bread session, Craig Martell, chief AI officer for Cohesity; and Matt McVaney, chief revenue office for BombBomb, discussed finding common ground between operations and tech to make data useful, and better understand the demands on either side of this dilemma.InformationWeek's podcast is evolving. The "Breaking Bread" format brings together one CIO, CTO, or other C-suite tech leader to discuss a challenge with an executive from the operations side of another organization.The intent is to allow them the space to exchange ideas and pain points from their respective vantages, and foster discussions that generate new insights for their organizations as well as the audience.About the AuthorJoao-Pierre S. RuthSenior EditorJoao-Pierre S. Ruth covers tech policy, including ethics, privacy, legislation, and risk; fintech; code strategy; and cloud & edge computing for InformationWeek. He has been a journalist for more than 25 years, reporting on business and technology first in New Jersey, then covering the New York tech startup community, and later as a freelancer for such outlets as TheStreet, Investopedia, and Street Fight.See more from Joao-Pierre S. RuthWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like

TechTarget and Informa Tech’s Digital Business Combine.TechTarget and InformaTechTarget and Informa Tech’s Digital Business Combine.Together, we power an unparalleled network of 220+ online properties covering 10,000+ granular topics, serving an audience of 50+ million professionals with original, objective content from trusted sources. We help you gain critical insights and make more informed decisions across your business priorities.Breaking Bread: Do You Understand Your Data?Breaking Bread: Do You Understand Your Data?As IT leadership wrangles with data governance and policies, can operations glean useful information without stepping on a few toes?Joao-Pierre S. Ruth, Senior EditorMay 19, 2025What makes data actionable and real for an organization?Many enterprises capture data as part of their operations and activity but taking that information to another usable level can be a challenge. There may be security concerns, privacy regulations to adhere to, conflicting requirements among stakeholders within the organization.If the world runs on data that fuels the software that gets put into action, how do you separate the wheat from the chaff? And how do you not step on too many toes along the way?In this Breaking Bread session, Craig Martell, chief AI officer for Cohesity; and Matt McVaney, chief revenue office for BombBomb, discussed finding common ground between operations and tech to make data useful, and better understand the demands on either side of this dilemma.InformationWeek's podcast is evolving. The "Breaking Bread" format brings together one CIO, CTO, or other C-suite tech leader to discuss a challenge with an executive from the operations side of another organization.The intent is to allow them the space to exchange ideas and pain points from their respective vantages, and foster discussions that generate new insights for their organizations as well as the audience.About the AuthorJoao-Pierre S. RuthSenior EditorJoao-Pierre S. Ruth covers tech policy, including ethics, privacy, legislation, and risk; fintech; code strategy; and cloud & edge computing for InformationWeek. He has been a journalist for more than 25 years, reporting on business and technology first in New Jersey, then covering the New York tech startup community, and later as a freelancer for such outlets as TheStreet, Investopedia, and Street Fight.See more from Joao-Pierre S. RuthWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like

Sr. Manager, Governance Risk and ComplianceUnityBengaluru, India43 minutes agoApplyThe opportunityJoin our Risk Assurance and Advisory team to provide independent assurance to contribute and improve Unity’s operation. Reporting to the Head of Internal Audit, this role will be a key member of our team, supporting Unity’s Risk Assurance and Advisory programs, including the Sarbanes-Oxley (SOX) compliance and Operational Audits. You will help Unity achieve its business objectives by bringing a systematic, objective, and disciplined approach to evaluating the effectiveness of the company’s governance, risk management, compliance, and control practices.What you'll be doingEstablish and own relationships with key partners as their strategic risk advisor on establishing internal controls and process efficiencies, providing insight and direction on risk managementLead a team of risk and control professionals (both internal and external resources) who possess the knowledge and technical capability to drive value-based audits & technology SOX compliance evaluationCollaborate cross-functionally across technology teams, external auditor, and co-sourced provider.Lead and manage risk-based integrated audits and/or process reviews, including cyber security and privacy auditsSupport the Risk Assurance & Advisory team in facilitating Unity’s Enterprise Risk Management program and updating the technology audit planSupport other projects, as needed.What we're looking forDeep knowledge of technology marketplace environments/controls including both 3rd party and home-grown systemsMission-driven, enthusiastic to learn and grow; team-oriented with a positive attitude and able to work in fast paced environmentAbility to work collaboratively, present technical concepts to people at all levels of the organization, make actionable recommendations and use influence to ensure executionStrong risk management experience, including: performing assessments and audits, driving issue impact discussions, and the ability to think strategically about remediation strategiesYou might also haveCISA, CISSP, or other professional certificationsBig 4/consulting and industry background in AdTech or Software industryPositive "self-starter" with focus to drive positive impact at UnityAdditional informationRelocation support is not available for this position.Work visa/immigration sponsorship is not available for this positionLife at UnityUnity [NYSE: U] is the leading platform to create and grow games and interactive experiences. Creators, ranging from game developers to artists, architects, automotive designers, infrastructure experts, filmmakers, and more, use Unity to bring their imaginations to life across multiple platforms, from mobile, PC, and console, to spatial computing.As of the third quarter of 2024, more than 70% of the top 1,000 mobile games were made with Unity, and in 2024, Made with Unity applications had an average of 3.7 billion downloads per month. For more information, please visit Unity.Unity is an equal opportunity employer committed to fostering an inclusive, innovative environment with the best employees. Therefore, we provide employment opportunities without regard to age, race, color, ancestry, national origin, disability, gender, or any other protected status in accordance with applicable law. If there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.Headhunters and recruitment agencies may not submit resumes/CVs through this website or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity.Your privacy is important to us. Please take a moment to review our Prospect and Applicant Privacy Policies. Should you have any concerns about your privacy, please contact us at DPO@unity.com.#SEN #LI-NV1 Create Your Profile — Game companies can contact you with their relevant job openings. Apply

Cybersecuritygetty Each May, the cybersecurity industry converges in San Francisco for the RSA Conference. This year’s gathering, attracting more than 45,000 attendees, highlighted several transformative trends reshaping digital asset protection, with AI unsurprisingly generating the most discussion. Beyond AI, we're witnessing an acceleration of platformization, which is enabling the rise of comprehensive security providers, cybersecurity moving directly into the data path, and growing threats to operational technology and industrial infrastructure. The Three Faces of AI: Protector, Protected, and Predator Artificial intelligence is emerging as a game-changing force in cybersecurity, playing three distinct roles in an increasingly complex digital landscape. Protecting Enterprise AI Today's enterprises are on the verge of deploying "agentic AI" systems that work independently — scheduling meetings, analyzing data, writing code, and making decisions with minimal human oversight. These AI agents create new security risks as they access sensitive company data and systems. Organizations now need specialized protection to prevent their AI helpers from being compromised. IBM leads this space with its watsonx platform, delivering comprehensive enterprise compliance and governance tools. Domino Data Labs takes compliance to an even higher level for highly regulated industries, while CrowdStrike brings its identity expertise to the agentic AI world through Falcon Identity Protection. As CrowdStrike's president, Michael Sentonas, told me at the event, "Every AI agent is an endpoint that must be protected." Palo Alto Networks unveiled its comprehensive new Prisma AIRS platform to protect AI workloads. The company also announced its intent to acquire Protect AI, an established provider of security solutions for AI applications. Several startups are making strides in this area, including CalypsoAI, with its platform ensuring AI model safety for inference, MIND, with its unique approach to data loss prevention and insider risk management for AI applications, and Cyberhaven, which provides visibility over AI tool usage. AI as a Digital Defender AI is indispensable for security teams protecting vast digital environments against rapidly evolving threats. Modern security systems can analyze billions of events to spot suspicious patterns, identify unusual behavior, automatically respond to threats, and predict potential attack vectors. These systems do this at a scale impossible for human analysts. SentinelOne unveiled its Purple AI Athena, introducing agentic AI capabilities that emulate seasoned security analysts' decision-making processes. Palo Alto Networks enhanced its Charlotte AI platform with tools that automate incident triage and initiate policy-driven responses. Defending Against AI-Powered Attacks Most concerning is how threat actors are wielding AI as a weapon. Cybercriminals and nation-states now use AI to generate convincing phishing emails, create deepfakes for elaborate scams, discover vulnerabilities, and customize attacks based on individuals' online behavior. The most alarming development is how AI democratizes cybercrime, making advanced hacking techniques accessible to almost anyone. This arms race means AI-powered attacks require equally sophisticated AI defenses. While a trend, there isn't dedicated protection against AI attacks. Instead, nearly every company I spoke with at the event recognized the threat and is enhancing offerings to better address it. Platformization: The Rise of Security Giants Cybersecurity platforms function as digital superstores — integrated systems providing multiple security functions through a single interface. Benefits include simplified management through unified dashboards, improved threat detection through tool communication, reduced costs through consolidated licensing, consistent protection across systems, and faster incident response. Palo Alto Networks pioneered this trend, evolving from a network security provider to a comprehensive platform. CrowdStrike expanded from endpoint protection to a full-scale security platform with its Falcon offering, while SentinelOne transformed its Singularity solution into a broader platform. Tech giants like Microsoft, Cisco, and Google follow similar paths. Protection Moves into the Data Path Traditional cybersecurity has functioned as a wall around digital assets. A new approach is emerging: embedding protection directly into data management systems. This "in-line" security continuously monitors and safeguards data as it moves, is accessed, and is stored. Nearly every storage provider is doing something in this space. NetApp has integrated real-time malware scanning directly into their storage systems, automatically checking files for threats whenever they're saved or accessed, all without requiring separate security software or adding delays. IBM offers similar technology across its storage portfolio. Infinidat, the only storage vendor exhibiting at RSAC this year, takes an even bolder approach by offering actual guarantees around data recovery after a cyberattack. Its storage solutions include built-in capabilities ensuring organizations can recover data without paying ransom, backed by a financial guarantee with specific recovery time objectives. Pure Storage supports immutable snapshots and offers comprehensive SLAs for ransomware recovery in its Evergreen//One storage-as-a-service offering. Hewlett-Packard Enterprise recently announced similar capabilities for its B10000 Alletra storage systems. OT and the Edge: The New Security Frontier Operational Technology refers to the hardware and software that monitor and control physical equipment and processes. These systems run power plants, water treatment facilities, manufacturing lines, oil refineries, and transportation networks. For decades, these industrial systems were largely isolated from the internet, creating an "air gap" that made them difficult to hack remotely. That world has disappeared. Today's industrial facilities are rapidly connecting their operational systems to corporate networks and the internet, creating dangerous new attack surfaces. Unlike typical cyberattacks that might result in stolen data or financial losses, attacks on industrial systems can have physical, real-world consequences. Securing these environments requires specialized approaches: Asset Visibility: Organizations are investing in specialized tools to discover and monitor all industrial devices and systems OT-Specific Security: Implementing security technologies specifically designed for industrial systems Network Segmentation: Dividing networks into isolated zones to prevent attackers from moving from business systems to critical operational systems Supply Chain Security: Securing complex equipment and software supply chains against tampering and compromises A small but growing segment of the cybersecurity industry is dedicated to protecting these environments. Cisco's Industrial Threat Platform directly addresses these concerns, announcing new capabilities at RSAC. It integrates new threat intelligence from Cisco Vulnerability Management and provides new industrial zone protection in its Cyber Vision and Secure Firewall. TXOne Networks, a startup specializing in OT protection, showcased its Stellar endpoint protection solution. Stellar enables AI-powered threat detection, zero-trust segmentation, and real-time behavioral monitoring for OT environments. The Road Ahead: Cybersecurity's Evolution The trends highlighted at this year's RSA Conference paint a clear picture of cybersecurity's future. The industry is moving from isolated security tools to comprehensive platforms, from perimeter defenses to protection embedded in the data path, and from primarily digital concerns to securing the physical-digital intersection. Meanwhile, AI has introduced a complex three-dimensional challenge: it must be protected as a valuable enterprise asset, leveraged as a powerful defensive tool, and countered as a formidable weapon in attackers' arsenals. Enterprises that adapt to this new reality will be positioned to thrive in an increasingly connected world. Those that cling to outdated security paradigms may find themselves dangerously exposed. The message from RSAC 2025 is clear: cybersecurity is no longer just an IT concert but a fundamental business imperative requiring strategic investment and continuous evolution. As attack surfaces expand and threats grow more sophisticated, the most successful organizations will embrace these emerging trends and build security into every aspect of their operations. Robust cybersecurity is about more than just avoiding disaster; Cybersecurity is about enabling innovation and growth with confidence. Disclosure: Steve McDowell is an industry analyst, and NAND Research is an industry analyst firm, that engages in, or has engaged in, research, analysis and advisory services with many technology companies, including HPE, IBM, Infinidat, NetApp, Pure Storage, and SentinelOne. Mr. McDowell does not hold any equity positions with any company mentioned.

Author(s): Sophia Banton Originally published on Towards AI. Steve Jobs once said, “Technology is nothing. What’s important is that you have a faith in people, that they’re basically good and smart, and if you give them tools, they’ll do wonderful things with them.” These were the tools that were given to me so that I could fly, by the giants on whose shoulders I stand. PyMOL: Seeing Beauty in Science I remember it like it was yesterday. I was working on an assignment in class. Following the steps carefully, I watched as it happened: proteins appeared as beautiful ribbons on the screen, their intricate structures swirling in vibrant colors. In that moment, I was captivated by PyMOL, a computer program for viewing biological molecules in 3D. Warren Delano’s PyMOL wasn’t just a visualization tool — it was a window into the elegance of science. PyMOL taught me that data is more than information — it’s art, and that technology and science are deeply intertwined. It was also my first interaction with open-source software — free tools that bring opportunities to anyone, anywhere. This insight, the power of accessible technology, has endured among my fundamental beliefs. With PyMOL, I found the gateway to the next chapter of my journey. An image I created with PyMOL was central to my first scientific publication. That image remains on the opening page of my portfolio today, a testament to the power of visualization. That publication led to my first professional role in science, where I discovered the tool that would open the door to endless possibilities. R: Freedom to Create with Code In that role I discovered R, a programming language for graphics and statistics — it was love at first byte. Unlike PyMOL, R was my first self-taught adventure, mastered at home with just an Amazon-bought book and determination. While other programming languages felt like strict rule books, R was an artist’s palette. Its quirky symbols and flexible approach felt like an invitation to be creative with code. R became my key to exploring data, ultimately unlocking the most impactful opportunity on my path. The data manipulation skills I developed in R led me to the frontiers of innovation — a new role in biomedical research. R wasn’t just a tool; it became a trusted companion for weaving together complex data — from genomics to clinical information. With R, data analysis was just the beginning. The next tool allowed me to mesmerize audiences with the beauty of data. ggplot2: Turning Data into Colorful Stories Like my discovery of PyMOL, my first encounter with Hadley Wickham’s ggplot2 resonated deeply. This visualization toolkit for R, built on the principles of the grammar of graphics (hence the gg), transcended data into stories told through colors, patterns, and shapes. I wasn’t just analyzing data anymore; I was uncovering hidden stories. These plots had elements of style that would impress Van Gogh — themes, borders, and vibrant palettes. The result? Multiple scientific publications and a new identity: “the woman who makes pretty plots”. But like PyMOL and R, ggplot2 taught me that success isn’t just about achievements — it’s about empowering others. Inspired by the open-source community, I created an online ggplot2 course. The most rewarding moment? When a colleague from another continent recognized me from my course and warmly shook my hand. Yet ggplot2 wasn’t the final chapter — it was another stepping-stone on my road of discovery. Plotly: Making Data Come Alive ggplot2 revealed the beauty of data, but Plotly in R taught me how to make visualizations interactive with clickable charts and dynamic features. Visualizations were no longer just static images on screens — they could come alive. Plotly also allowed me to fine-tune my skills in another programming language called Python. Plotly in Python opened doors to freelance opportunities in data visualization. These projects boosted both my skills and confidence. These experiences prepared me for my leap into industry, where I would turn tools into solutions. But before that transition, there was one more tool in R to master — it would become my most trusted companion. R Shiny: The Catalyst for Transformation R had become the backbone of my career when I stumbled upon something unexpected — R Shiny, a tool for creating web apps in R. I stared at the screen in awe, remembering the first time I saw protein ribbons in PyMOL. I used online resources to teach myself R Shiny. R Shiny brought everything together: R’s analytics, ggplot2’s beauty, and Plotly’s interactivity. Now I could share data through intuitive web apps, no more creating endless PowerPoint presentations. Shiny became my treasured companion and the cornerstone of my budding career. R Shiny wasn’t just a tool — it was a career catalyst. Making apps wasn’t part of my original plan — honestly, there was no plan. But learning R Shiny gave me the confidence to tackle new challenges beyond the academic environment I called home. Shiny in Action: Empowering Users and Solving Problems I joined a startup where I used Shiny to detect fraud — my first venture beyond academia into the world of technology professionals. Then came an opportunity that would tie all my tools together. Still new to industry, I was unfamiliar with recruiters, hiring practices and corporate culture. But I did what I had always done, I used my best tools. The hiring process required a hands-on use case, so I built a Shiny app in two intense days. That app got me the job. Within this new role, R Shiny gave me my first industry publication and first published app. Like PyMOL opened the door to science, R Shiny introduced me to the complexities of working in industry. Each new app connected me with different business functions — from Marketing to Medical Affairs — teaching me about collaboration, resilience, and servant leadership. These experiences prepared me for an unexpected shift — the rise of AI that transformed how we interact with technology. Generative AI: Redefining Interaction The release of ChatGPT marked a turning point in how people interacted with technology. I turned to my trusted friend — R Shiny — to quickly build examples of what this new technology could do. Within two months of the release of ChatGPT, we had our first generative AI application running. Once again, R Shiny proved to be an invaluable tool for embracing the future. By the next year, generative AI had infiltrated industries, creating new opportunities for innovation. At work, I had the chance to contribute to an exciting generative AI project. The increasing demands for flexibility led me to transition to Shiny for Python, combining Shiny’s elegance with Python’s vast AI resources. The application proved successful enough to move from a prototype to an operational solution within the company. Shiny had evolved, and so had I. No longer just “the woman who makes pretty plots and apps”, I stepped into the future of AI with my trusted companion at my side. Because regardless of the engines that power AI, the need to make data accessible and interactive will always remain. My Tools, My Teammates Looking back, these weren’t just tools — they were teammates. PyMOL revealed the beauty of science. R offered boundless creativity. ggplot2 and Plotly turned data into stories. Shiny transformed me from a scientist to an innovator, ready for the AI revolution. Each tool shaped who I am, and together they taught me the most important lesson: technology’s true power lies not in the code, but in how it empowers people to do wonderful things. About the Author Sophia Banton is an Associate Director and AI Solution Lead in biopharma, specializing in Responsible AI governance, workplace AI adoption, and building and scaling AI solutions across IT and business functions. With a background in bioinformatics, public health, and data science, she brings an interdisciplinary lens to AI implementation — balancing technical execution, ethical design, and business alignment in highly regulated environments. Her writing explores the real-world impact of AI beyond theory, helping organizations adopt AI responsibly and sustainably. Connect with her on LinkedIn or explore more AI insights on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI

America’s quest to protect its semiconductor technology from China has taken increasingly dramatic turns over the past few years—from export bans to global restrictions—but the latest proposal from Congress ventures into unprecedented territory. Lawmakers are now pushing for mandatory GPS-style tracking embedded in every AI chip exported from the United States, essentially turning advanced semiconductors into devices that report their location back to Washington.On May 15, 2025, a bipartisan group of eight House representatives introduced the Chip Security Act, which would require companies like Nvidia to embed location verification mechanisms in their processors before export. This represents perhaps the most invasive approach yet in America’s technological competition with China, moving far beyond restricting where chips can go to actively monitoring where they end up.The mechanics of AI chip surveillanceUnder the proposed Chip Security Act, AI chip surveillance would become mandatory for all “covered integrated circuit products”—including those classified under Export Control Classification Numbers 3A090, 3A001.z, 4A090, and 4A003.z. Companies like Nvidia would be required to embed location verification mechanisms in their AI chips before export, reexport, or in-country transfer to foreign nations.Representative Bill Huizenga, the Michigan Republican who introduced the House bill, stated that “we must employ safeguards to help ensure export controls are not being circumvented, allowing these advanced AI chips to fall into the hands of nefarious actors.” His co-lead, Representative Bill Foster—an Illinois Democrat and former physicist who designed chips during his scientific career—added, “I know that we have the technical tools to prevent powerful AI technology from getting into the wrong hands.”The legislation goes far beyond simple location tracking. Companies would face ongoing surveillance obligations, required to report any credible information about chip diversion, including location changes, unauthorized users, or tampering attempts. This creates a continuous monitoring system that extends indefinitely beyond the point of sale, fundamentally altering the relationship between manufacturers and their products.Cross-party support for technology controlPerhaps most striking about this AI chip surveillance initiative is its bipartisan nature. The bill enjoys broad support across party lines, co-led by House Select Committee on China Chairman John Moolenaar and Ranking Member Raja Krishnamoorthi. Other cosponsors include Representatives Ted Lieu, Rick Crawford, Josh Gottheimer, and Darin LaHood.Moolenaar said that “the Chinese Communist Party has exploited weaknesses in our export control enforcement system—using shell companies and smuggling networks to divert sensitive US technology.” The bipartisan consensus on AI chip surveillance reflects how deeply the China challenge has penetrated American political thinking, transcending traditional partisan divisions.The Senate has already introduced similar legislation through Senator Tom Cotton, suggesting that semiconductor surveillance has broad congressional support. Coordination between chambers indicates that some form of AI chip surveillance may become law regardless of which party controls Congress.Technical challenges and implementation questionsThe technical requirements for implementing AI chip surveillance raise significant questions about feasibility, security, and performance. The bill mandates that chips implement “location verification using techniques that are feasible and appropriate” within 180 days of enactment, but provides little detail on how such mechanisms would work without compromising chip performance or introducing new vulnerabilities.For industry leaders like Nvidia, implementing mandatory surveillance technology could fundamentally alter product design and manufacturing processes. Each chip would need embedded capabilities to verify its location, potentially requiring additional components, increased power consumption, and processing overhead that could impact performance—precisely what customers in AI applications cannot afford.The bill also grants the Secretary of Commerce broad enforcement authority to “verify, in a manner the Secretary determines appropriate, the ownership and location” of exported chips. This creates a real-time surveillance system where the US government could potentially track every advanced semiconductor worldwide, raising questions about data sovereignty and privacy.Commercial surveillance meets national securityAI chip surveillance proposal represents an unprecedented fusion of national security imperatives with commercial technology products. Unlike traditional export controls that simply restrict destinations, the approach creates ongoing monitoring obligations that blur the lines between private commerce and state surveillance.Representative Foster’s background as a physicist lends technical credibility to the initiative, but it also highlights how scientific expertise can be enlisted in geopolitical competition. The legislation reflects a belief that technical solutions can solve political problems—that embedding surveillance capabilities in semiconductors can prevent their misuse.Yet the proposed law raises fundamental questions about the nature of technology export in a globalized world. Should every advanced semiconductor become a potential surveillance device? How will mandatory AI chip surveillance affect innovation in countries that rely on US technology? What precedent does this set for other nations seeking to monitor their technology exports?Accelerating technological decouplingThe mandatory AI chip surveillance requirement could inadvertently accelerate the development of alternative semiconductor ecosystems. If US chips come with built-in tracking mechanisms, countries may intensify efforts to develop domestic alternatives or source from suppliers without such requirements.China, already investing heavily in semiconductor self-sufficiency following years of US restrictions, may view these surveillance requirements as further justification for technological decoupling. The irony is striking: efforts to track Chinese use of US chips may ultimately reduce their appeal and market share in global markets.Meanwhile, allied nations may question whether they want their critical infrastructure dependent on chips that can be monitored by the US government. The legislation’s broad language suggests that AI chip surveillance would apply to all foreign countries, not just adversaries, potentially straining relationships with partners who value technological sovereignty.The future of semiconductor governanceAs the Trump administration continues to formulate its replacement for Biden’s AI Diffusion Rule, Congress appears unwilling to wait. The Chip Security Act represents a more aggressive approach than traditional export controls, moving from restriction to active surveillance in ways that could reshape the global semiconductor industry.This evolution reflects deeper changes in how nations view technology exports in an era of great power competition. The semiconductor industry, once governed primarily by market forces and technical standards, increasingly operates under geopolitical imperatives that prioritize control over commerce.Whether AI chip surveillance becomes law depends on congressional action and industry response. But the bipartisan support suggests that some form of semiconductor monitoring may be inevitable, marking a new chapter in the relationship between technology, commerce, and national security.Conclusion: The end of anonymous semiconductors from America?The question facing the industry is no longer whether the US will control technology exports, but how extensively it will monitor them after they leave American shores. In this emerging paradigm, every chip becomes a potential intelligence asset, and every export a data point in a global surveillance network.The semiconductor industry now faces a critical choice: adapt to a future where products carry their own tracking systems, or risk being excluded from the US market entirely. As Congress pushes for mandatory AI chip surveillance, we may be witnessing the end of anonymous semiconductors and the beginning of an era where every processor knows exactly where it belongs—and reports back accordingly.See also: US-China tech war escalates with new AI chips export controlsWant to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here.

May 16, 2025The Hacker NewsZero Trust / Data Protection Data is the lifeblood of productivity, and protecting sensitive data is more critical than ever. With cyber threats evolving rapidly and data privacy regulations tightening, organizations must stay vigilant and proactive to safeguard their most valuable assets. But how do you build an effective data protection framework? In this article, we'll explore data protection best practices from meeting compliance requirements to streamlining day-to-day operations. Whether you're securing a small business or a large enterprise, these top strategies will help you build a strong defense against breaches and keep your sensitive data safe. 1. Define your data goals When tackling any data protection project, the first step is always to understand the outcome you want. First, understand what data you need to protect. Identify your crown jewel data, and where you THINK it lives. (It's probably more distributed than you expect, but this is a key step to help you define your protection focus.) Work with business owners to find any data outside the typical scope that you need to secure. This is all to answer the question: "What data would hurt the company if it were breached?" Second, work with the C-suit and board of directors to define what your data protection program will look like. Understand your budget, your risk tolerance to data loss, and what resources you have (or may need). Define how aggressive your protection program will be so you can balance risk and productivity. All organizations need to strike a balance between the two. 2. Automate data classification Next, begin your data classification journey—that is, find your data and catalog it. This is often the most difficult step in the journey, as organizations create new data all the time. Your first instinct may be to try to keep up with all your data, but this may be a fool's errand. The key to success is to have classification capabilities everywhere data moves (endpoint, inline, cloud), and rely on your DLP policy to jump in when risk arises. (More on this later.) Automation in data classification is becoming a lifesaver thanks to the power of AI. AI-powered classification can be faster and more accurate than traditional ways of classifying data with DLP. Ensure any solution you are evaluating can use AI to instantly uncover and discover data without human input. 3. Focus on zero trust security for access control Adopting a zero trust architecture is crucial for modern data protection strategies to be effective. Based on the maxim "never trust, always verify," zero trust assumes security threats can come from inside or outside your network. Every access request is authenticated and authorized, greatly reducing the risk of unauthorized access and data breaches. Look for a zero trust solution that emphasizes the importance of least-privileged access control between users and apps. With this approach, users never access the network, reducing the ability for threats to move laterally and propagate to other entities and data on the network. The principle of least privilege ensures that users have only the access they need for their roles, reducing the attack surface. 4. Centralize DLP for consistent alerting Data loss prevention (DLP) technology is the core of any data protection program. That said, keep in mind that DLP is only a subset of a larger data protection solution. DLP enables the classification of data (along with AI) to ensure you can accurately find sensitive data. Ensure your DLP engine can consistently alert correctly on the same piece of data across devices, networks, and clouds. The best way to ensure this is to embrace a centralized DLP engine that can cover all channels at once. Avoid point products that bring their own DLP engine (endpoint, network, CASB), as this can lead to multiple alerts on one piece of moving data, slowing down incident management and response. Look to embrace Gartner's security service edge approach, which delivers DLP from a centralized cloud service. Focus on vendors that support the most channels so that, as your program grows, you can easily add protection across devices, inline, and cloud. 5. Ensure blocking across key loss channels Once you have a centralized DLP, focus on the most important data loss channels to your organization. (You'll need to add more channels as you grow, so ensure your platform can accommodate all of them and grow with you.) The most important channels can vary, but every organization focuses on certain common ones: Web/Email: The most common ways users accidentally send sensitive data outside the organization. SaaS data (CASB): Another common loss vector, as users can easily share data externally. Endpoint: A key focus for many organizations looking to lock down USB, printing, and network shares. Unmanaged devices/BYOD: If you have a large BYOD footprint, browser isolation is an innovative way to secure data headed to these devices without an agent or VDI. Devices are placed in an isolated browser, which enforces DLP inspection and prevents cut, paste, download, or print. (More on this later.) SaaS posture control (SSPM/supply chain): SaaS platforms like Microsoft 365 can often be misconfigured. Continuously scanning for gaps and risky third-party integrations is key to minimizing data breaches. IaaS posture control (DSPM): Most companies have a lot of sensitive data across AWS, Azure, or Google Cloud. Finding it all, and closing risky misconfigurations that expose it, is the driver behind data security posture management (DSPM). 6. Understand and maintain compliance Getting a handle on compliance is a key step for great data protection. You may need to keep up with many different regulations, depending on your industry (GDPR, PCI DSS, HIPAA, etc.). These rules are there to make sure personal data is safe and organizations are handling it the right way. Stay informed on the latest mandates to avoid fines and protect your brand, all while building trust with your customers and partners. To keep on top of compliance, strong data governance practices are a must. This means regular security audits, keeping good records, and making sure your team is well-trained. Embrace technological approaches that help drive better compliance, such as data encryption and monitoring tools. By making compliance part of your routine, you can stay ahead of risks and ensure your data protection is both effective and in line with requirements. 7. Strategize for BYOD Although not a concern for every organization, unmanaged devices present a unique challenge for data protection. Your organization doesn't own or have agents on these devices, so you can't ensure their security posture or patch level, wipe them remotely, and so on. Yet their users (like partners or contractors) often have legitimate reasons to access your critical data. You don't want sensitive data to land on a BYOD endpoint and vanish from your sight. Until now, solutions to secure BYOD have revolved around CASB reverse proxies (problematic) and VDI approaches (expensive). Browser isolation provides an effective and eloquent way to secure data without the cost and complexity of those approaches. By placing BYOD endpoints in an isolated browser (part of the security service edge), you can enforce great data protection without an endpoint agent. Data is streamed to the device as pixels, allowing interaction with the data but preventing download and cut/paste. You can also apply DLP inspection to the session and data based on your policy. 8. Control your cloud posture with SSPM and DSPM Cloud posture is one of the most commonly overlooked aspects of data hygiene. SaaS platforms and public clouds have many settings that DevOps teams without security expertise can easily overlook. The resulting misconfigurations can lead to dangerous gaps that expose sensitive data. Many of the largest data breaches in history have happened because such gaps let adversaries walk right in. SaaS security posture management (SSPM) and data security posture management (DSPM for IaaS) are designed to uncover and help remediate these risks. By leveraging API access, SSPM and DSPM can continuously scan your cloud deployment, locate sensitive data, identify misconfigurations, and remediate exposures. Some SSPM approaches also feature integrated compliance with frameworks like NIST, ISO, and SOC 2. 9. Don't forget about data security training Data security training is often where data protection programs fall apart. If users don't understand or support your data protection goals, dissent can build across your teams and derail your program. Spend time building a training program that highlights your objectives and the value data protection will bring the organization. Ensure upper management supports and sponsors your data security training initiatives. Some solutions offer built-in user coaching with incident management workflows. This valuable feature allows you to notify users about incidents via Slack or email for justification, education, and policy adjustment if needed. Involving users in their incidents helps promote awareness of data protection practices as well as how to identify and safely handle sensitive content. 10. Automate incident management and workflows Lastly, no data protection program would be complete without day-to-day operations. Ensuring your team can efficiently manage and quickly respond to incidents is critical. One way to ensure streamlined processes is to embrace a solution that enables workflow automation. Designed to automate common incident management and response tasks, this feature can be a lifesaver for IT teams. By saving time and money while improving response times, IT teams can do more with less. Look for solutions that have a strong workflow automation offering integrated into the SSE to make incident management efficient and centralized. Bringing it all together Data protection is not a one-time project; it's an ongoing commitment. Staying informed of data protection best practices will help you build a resilient defense against evolving threats and ensure your organization's long-term success. Remember: investing in data protection is not just about mitigating risks and preventing data breaches. It's also about building trust, maintaining your reputation, and unlocking new opportunities for growth. Learn more at zscaler.com/security Found this article interesting? 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