Calyx Law LLP Announces New Partnership, Expands IP Leadership in Emerging Tech  FinancialContent

Posted on : June 3, 2025 By Tech World Times Technology  Rate this post This article walks you through five practical steps to implement network segmentation effectively, backed by real-world use cases that showcase its value in different industries. Networks are constantly expanding across offices, cloud services, remote users, and connected devices. With so many moving parts, security gaps can easily form. Once attackers breach a weak point, they often move freely across the network, targeting critical systems and sensitive data. That’s where network segmentation comes in. It’s a practical approach to divide your network into smaller, manageable zones to control access, limit exposure, and isolate threats before they spread. But simply deploying VLANs or access rules isn’t enough. True segmentation needs planning, alignment with your business, and the right mix of technology. Step 1: Assess and Map Your Current Network Start by figuring out what’s on your network and how it communicates. Inventory Devices and Applications: List all system servers, user machines, IoT devices, cloud assets. Map Data Flows: Understand how applications and services interact. Which systems talk to each other? What ports and protocols are used? Identify Critical Assets: Highlight the systems that handle sensitive data, such as payment processing, health records, or intellectual property. Tip: Network discovery tools or NAC solutions can automate asset inventory and reveal communication paths you might miss. Step 2: Define Segmentation Goals and Policies Once you understand your environment, it’s time to set your objectives. Security Objectives: Do you want to reduce lateral movement, isolate sensitive systems, or meet a compliance mandate? Business Alignment: Segment by business unit, sensitivity of data, or risk profile-whatever makes the most operational sense. Compliance Requirements: PCI DSS, HIPAA, and other standards often require network segmentation. Example: A healthcare provider might create separate zones for patient records, lab equipment, guest Wi-Fi, and billing systems. Step 3: Choose the Right Segmentation Method Segmentation can be done in several ways. The right approach depends on your infrastructure goals and types: a. Physical Segmentation Use separate routers, switches, and cables. This offers strong isolation but can be costly and harder to scale. b. Logical Segmentation (VLANs/Subnets) Group devices into virtual segments based on function or department. It’s efficient and easier to manage in most environments. c. Micro segmentation Control access at the workload or application level using software-defined policies. Ideal for cloud or virtualized environments where you need granular control. d. Cloud Segmentation In the cloud, segmentation happens using security groups, VPCs, and IAM roles to isolate workloads and define access rules. Use a combination- VLANs for broader segmentation and micro segmentation for finer control where it matters. Step 4: Implement Controls and Monitor Traffic Time to put those policies into action. Firewalls and ACLs: Use access controls to manage what can move between zones. Block anything that isn’t explicitly allowed. Zero Trust Principles: Never assume trust between segments. Always validate identity and permissions. Monitoring and Alerts: Use your SIEM, flow monitoring tools, or NDR platform to watch for unusual traffic or policy violations. Common Pitfall: Avoid “allow all” rules between segments, it defeats the purpose. Step 5: Test, Validate, and Fine-Tune Even a well-designed segmentation plan can have gaps. Regular validation helps ensure it works as expected. Penetration Testing: Simulate attacks to check if boundaries hold. Review Policies: Business needs to change your segmentation strategy too. Performance Monitoring: Make sure segmentation doesn’t impact legitimate operations or application performance. Automation tools can help simplify this process and ensure consistency. Real-World Use Cases of Network Segmentation 1. Healthcare – Protecting Patient Data and Devices Hospitals use segmentation to keep medical devices, patient records, and visitor Wi-Fi on separate zones. This prevents an infected guest device from interfering with critical systems. Result: Reduced attack surface and HIPAA compliance. 2. Manufacturing – Isolating Industrial Systems Production environments often have fragile legacy systems. Segmenting OT (Operational Technology) from IT ensures ransomware or malware doesn’t disrupt manufacturing lines. Result: More uptime and fewer operational risks. 3. Finance – Securing Payment Systems Banks and payment providers use segmentation to isolate cardholder data environments (CDE) from the rest of the corporate network. This helps meet PCI DSS and keeps sensitive data protected. Result: Easier audits and stronger data security. 4. Education – Managing High-Volume BYOD Traffic Universities segment student Wi-Fi, research labs, and administrative systems. This keeps a vulnerable student device from spreading malware to faculty or internal systems. Result: Safer environment for open access campuses. 5. Cloud – Segmenting Apps and Microservices In the cloud, developers use security groups, VPCs, and IAM roles to isolate applications and limit who can access what. This reduces risk if one workload is compromised. Result: Controlled access and better cloud hygiene. Common Challenges Legacy Tech: Older devices may not support modern segmentation. Lack of Visibility: Hard to secure what you don’t know exists. Operational Hiccups: Poorly planned segmentation can block business workflows. Policy Complexity: Keeping access rules up to date across dynamic environments takes effort. Best Practices Start with High-Risk Areas: Prioritize zones handling sensitive data or vulnerable systems. Keep Documentation Updated: Maintain clear diagrams and policy records. Align Teams: Get buy-in from IT, security, and business units. Automate Where You Can: Especially for monitoring and policy enforcement. Review Regularly: Networks evolve- so should your segmentation. Final Thoughts Segmentation isn’t about creating walls it’s about building smart pathways. Done right, it helps you take control of your network, reduce risk, and respond faster when something goes wrong. It’s a foundational layer of cybersecurity that pays off in resilience, compliance, and peace of mind. About the Author: Prajwal Gowda is a cybersecurity expert with 10+ years of experience. He has built businesses and was a Business Unit Head for Compliance and Testing services. Currently, he is the Chief Technology Officer at Ampcus Cyber, leading the company’s technology strategy and innovation efforts. He has also been involved in the Payment Card Industry, Software Security Framework, ISO 27001 Controls Gap Analysis, ISMS, Risk Analysis, OCTAVE, ISO 27005, Information Security Audit and Network Security. Prajwal is a Master Trainer who has conducted 100+ cybersecurity training sessions worldwide. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

OIC 2025: Emerging Technology Panel  The Options Insider

Google has launched AI Edge Gallery, an open-source platform that enables developers to run advanced AI models directly on Android devices, with iOS support planned for a future release. Released under the Apache 2.0 license

ECOWAS at 50: What’s Next for West Africa’s Digital Future?  TechTrendsKE

The 12-year campaign to free Ross Ulbricht — the criminal mastermind behind the Silk Road, the original crypto-enabled dark web mail-order-drug emporium — finally ended in January, when newly-minted second term president Donald Trump officially pardoned the bitcoin criminal.Now, Ulbricht is picking up where he left off, getting his first taste of a world he left behind in 2013 when a federal judge sentenced him to two counts of life in prison, plus 40 years without parole.In sum, Ulbricht's feeling some culture shock. Taking the stage at the Bitcoin 2025 conference in Las Vegas this week, the drug kingpin was agog at the piles of kitschy tech products that had passed him by."When I walked out of prison a few months ago, I’d never seen a drone, used AI, or tried VR. I hadn’t even chatted with AI," Ulbricht marveled. "Now it's all hitting me at once — the freedom, the technology, the fact that I have a future again."He also basked in the glow of the crypto community's love — a parasocial following that lavished his clemency petition with over 600,000 signatures and an astonishing number of right-libertarian micro-celebrity endorsements."You didn't abandon me. You didn't forget me. You wrote me letters. You raised money for my defense. When I was silenced, you spoke up against the slander and the smears," the former dope baron lauded.Keep in mind, this wasn't a political activist jailed for protesting an unjust war, or a whistleblower whose life was destroyed after revealing a massive corporate fraud scheme.This is a guy who made millions selling drugs to teenagers and communities ravaged by the opioid crisis, resulting in at least six overdose deaths that we know of. (Not to mention the hitmen he tried to hire to take out his enemies, though he was unsuccessful.)At its peak in 2013, Ulbricht's Silk Road saw an estimated daily connected user base of 5.5 million. In just two years, it processed some $1.2 billion in illicit sales, according to the Department of Homeland Security.Through transaction fees, the service generated over $80 million worth of Bitcoin. Authorities in 2014 called it the "most sophisticated and extensive criminal marketplace on the internet."Of course, Ulbricht's saving grace isn't some ethical dilemma Trump has over incarceration or judicial mishandling, but the fact that he built Silk Road off the then-nascent blockchain. That single fact has cemented his status as a crypto superstar.For context, out of the 1.2 million citizens incarcerated in the US, over 360,000 of them face charges of nonviolent drug possession.In 2023, there were roughly 870,000 arrests for drug-related charges, the vast majority, or 87.8 percent of which were for drug possession — in other words, for carrying an impossibly tiny fraction of the volume that passed through Ulbricht's drug empire every minute.With his new lease on life, Ulbricht plans on paying it forward — not as an advocate for carceral reform or prison abolition, but to the crypto community of get-rich-quick schemers and granny scammers."With so much speed and chaos, it’s more important than ever to stay true to our principles," he told the crowd of cryptobros. "If we agree that we deserve freedom and that [crypto] decentralization secures it, we can stand together. Have each other’s backs, as you had mine. Freedom, decentralization, unity — stay true to these, and the future is ours."More on crypto: Visitors At This Bitcoin-Heated Spa Are Complaining About Mold and UTIsShare This Article

Key Takeaways AMD’s RX 9060 XT is set to launch on June 5th, 2025 in both 8GB and 16GB versions under the same name, creating confusion and backlash. Reviewers and gamers say 8GB of VRAM isn’t enough for modern gaming, especially at 1440p. AMD’s decision to showcase only the 16GB model in benchmarks raised concerns about transparency. This move mirrors Nvidia’s controversial RTX 4060 Ti rollout, suggesting an industry trend of misleading GPU marketing. It all started with a new GPU announcement. The AMD Radeon RX 9060 XT is set to launch, and on paper, it looks like a solid move. A $349 graphics card with 16GB of VRAM? Not bad. That’s more memory than some RTX 4070 cards. Sounds like AMD might finally be delivering some value again, right?  Well, yes and no.  Because right alongside that 16GB version, AMD is also releasing an 8GB version for $299. Same name, same chip, half the memory. And that’s where the internet lost it.  Déjà Vu: We’ve Seen This Trick Before If this sounds familiar, it’s because Nvidia pulled the same move with the RTX 4060 Ti.  They sold both 8GB and 16GB versions with the same branding, but a $100 price difference. The RTX 4060 Ti 8GB launched in May 2023, and the 16GB variant followed in July.  Source: Nvidia Gamers hated the confusion. Reviewers criticized the 8GB version’s lack of performance, especially in memory-heavy games, and the way Nvidia tried to sweep the difference under the rug.  Performance dipped significantly at 1440p, and stuttering was a problem even in some 1080p titles. The backlash was swift. Tech media slammed Nvidia for deceptive marketing, and buyers were left second-guessing which version they were getting.  We’ve seen this pattern before in Nvidia’s review restrictions around the RTX 5060, where early coverage was shaped by what reviewers were allowed to test – and what they weren’t.  It led to a mess of misinformation, bad value perceptions, and a very clear message: don’t confuse your customers. So naturally, AMD did it too.  It’s like watching two billion-dollar companies playing a game of ‘Who Can Confuse the Customer More.’ It’s not just about the money. It’s about trust, and AMD just dumped a bunch of it off a cliff.  Frank Azor Lights the Fuse on X The backlash started when AMD’s Director of Gaming Marketing, Frank Azor, took to X to defend the 8GB card.  He said that most gamers don’t need more than 8GB of VRAM and that the cheaper card still serves the mainstream crowd just fine.  It’s the same reasoning Nvidia used last year with the RTX 4060 Ti. That didn’t work then, and it isn’t working now.  Because when Steve from Hardware Unboxed sees a bad take like that, you know a flamethrower video is coming. And oh boy, did it come.  Hardware Unboxed Fires Back The backlash against AMD’s 8GB RX 9060 XT took off after a post from Hardware Unboxed on X called out the company’s defense of limited VRAM.  In response to AMD’s claim that most gamers don’t need more than 8GB of memory, Hardware Unboxed accused them of misleading buyers and building weaker products just to hit certain price points. The criticism gained traction fast. Tech YouTuber Vex picked up the story and added fuel to the fire by showing side-by-side gameplay comparisons.  In multiple games, the 8GB RX 9060 XT showed serious performance issues – stuttering, frame drops, and VRAM bottlenecks – while the 16GB version handled the same titles smoothly.  And yet, during the GPU’s official reveal, AMD only showed performance data for the 16GB card. There were no benchmarks for the 8GB version – not a single chart. That omission wasn’t lost on anyone. If AMD truly believed the 8GB model held up under modern gaming loads, they would have shown it. The silence speaks volumes.  Why This Actually Matters You might be thinking: ‘So what? Some games still run fine on 8GB. I only play Valorant.’ Sure. But the problem is bigger than that. Source: AMD Games are getting heavier. Even titles like Cyberpunk 2077, released in 2020, can eat up more than 8GB of VRAM. And with GTA 6 (still) on the horizon, do you really think game developers are going to keep optimizing for 8GB cards in 2025? That’s not how game development works. Developers target the most common setups, yes. But hardware also shapes software.  If everyone’s stuck with 8GB, games will be designed around that limit. That holds back progress for everyone.  It’s like trying to make a movie with a flip phone because some people still own one. Same Name, Different Game Another big issue is how these cards are named and sold.  The RX 9060 XT 16GB and RX 9060 XT 8GB are not clearly labeled as different products. They’re just two versions of the same GPU.  But that extra memory makes a huge difference.  In some games, the 8GB card performs dramatically worse. And yet, unless you know what to look for, you might walk into a store and buy the 8GB version thinking you’re getting the same performance.  You’re not. You’re getting a watered-down version with the same name and a silent asterisk. This isn’t just AMD’s Problem Nvidia started this mess with the 4060 Ti naming confusion. AMD just saw the outrage and decided to walk straight into the same buzzsaw.  It’s hard not to feel like both companies are treating consumers like they’re too dumb to notice. Spoiler: they noticed. And this whole ‘VRAM doesn’t matter’ argument? It’s already been debunked by dozens of reviewers.  If you’re spending over $300 on a graphics card in 2025, it needs to last more than a year or two. 8GB cards are already struggling. Buying one now is like buying a smartphone in 2025 with 64GB of storage. Sure, it works. Until it doesn’t. Steam Data Doesn’t Help AMD’s Case AMD and Nvidia both love to point at the Steam Hardware Survey. They say, ‘See? Most people still play at 1080p.’ And that’s true – for now. Source: Nvidia But what they leave out is that 1440p gaming is growing fast. More gamers are upgrading their setups because 1440p monitors are getting a lot more affordable.  Take the Pixio PXC277 Advanced, for instance – a 27-inch curved 1440p monitor with a 165Hz refresh rate and 1ms response time, all for $219.99. A few years ago, a screen like that would’ve cost you double. Now it’s entry-level. Gamers are ready to step up their experience. The only thing holding them back is GPU hardware that’s still stuck in 2020.  Planned Obsolescence in Disguise Here’s the worst part. Companies know full well that 8GB won’t cut it in 2026.  But they still sell it, knowing many gamers will only find out when it’s too late – when the stutters kick in, the textures disappear, or the next big title becomes unplayable. It’s planned obsolescence disguised as ‘choice.’ And while it’s great to have options at different price points, it should be clear which option is built to last – and which one is built to frustrate.  So, Is AMD Actually Screwed?  Not right now. In fact, they’re playing the game better than they used to.  They’ve learned from past pricing disasters and figured out how to get better launch-day headlines – even if it means faking the MSRP and letting street prices run wild.  But this kind of marketing comes at a cost. If AMD keeps making decisions that prioritize short-term wins over long-term trust, they’ll lose the very crowd that once rooted for them.  We don’t need two Nvidias. We need AMD to be different – to be better.  One Name, Two Very Different Cards The RX 9060 XT 16GB might be a good deal. But it’s being overshadowed by the 8GB version’s drama. And the longer AMD keeps playing games with memory and naming, the more it chips away at its hard-earned goodwill.  This whole mess could’ve been avoided with one simple move: name the 8GB card something else. Call it the RX 9055. Call it Lite or whatever. Just don’t make it look like the same card when it isn’t.  Until then, buyers beware. There’s more going on behind the box art than meets the eye.  Anya Zhukova is an in-house tech and crypto writer at Techreport with 10 years of hands-on experience covering cybersecurity, consumer tech, digital privacy, and blockchain. She’s known for turning complex topics into clear, useful advice that regular people can actually understand and use.  Her work has been featured in top-tier digital publications including MakeUseOf, Online Tech Tips, Help Desk Geek, Switching to Mac, and Make Tech Easier. Whether she’s writing about the latest privacy tools or reviewing a new laptop, her goal is always the same: help readers feel confident and in control of the tech they use every day.  Anya holds a BA in English Philology and Translation from Tula State Pedagogical University and also studied Mass Media and Journalism at Minnesota State University, Mankato. That mix of language, media, and tech has given her a unique lens to look at how technology shapes our daily lives.  Over the years, she’s also taken courses and done research in data privacy, digital security, and ethical writing – skills she uses when tackling sensitive topics like PC hardware, system vulnerabilities, and crypto security.  Anya worked directly with brands like Framework, Insta360, Redmagic, Inmotion, Secretlab, Kodak, and Anker, reviewing their products in real-life scenarios. Her testing process involves real-world use cases – whether it's stress-testing laptops for creative workloads, reviewing the battery performance of mobile gaming phones, or evaluating the long-term ergonomics of furniture designed for hybrid workspaces.  In the world of crypto, Anya covers everything from beginner guides to deep dives into hardware wallets, DeFi protocols, and Web3 tools. She helps readers understand how to use multisig wallets, keep their assets safe, and choose the right platforms for their needs.  Her writing often touches on financial freedom and privacy – two things she strongly believes should be in everyone’s hands. Outside of writing, Anya contributes to editorial style guides focused on privacy and inclusivity, and she mentors newer tech writers on how to build subject matter expertise and write responsibly.  She sticks to high editorial standards, only recommends products she’s personally tested, and always aims to give readers the full picture.  You can find her on LinkedIn, where she shares more about her work and projects.  Key Areas of Expertise: Consumer Tech (laptops, phones, wearables, etc.) Cybersecurity and Digital Privacy PC/PC Hardware Blockchain, Crypto Wallets, and DeFi In-Depth Product Reviews and Buying Guides Whether she’s reviewing a new wallet or benchmarking a PC build, Anya brings curiosity, care, and a strong sense of responsibility to everything she writes. Her mission? To make the digital world a little easier – and safer – for everyone.  View all articles by Anya Zhukova Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

Posted on : May 31, 2025 By Tech World Times Security Testing  Rate this post In a digital era where cyberattacks are increasing in frequency, complexity, and cost, organizations must stay one step ahead by investing in robust cybersecurity strategies. At the heart of this defense lies Cyber Security Threat Analysis, a process that helps businesses detect, understand, and respond to threats before they escalate. This comprehensive guide explores the fundamentals of threat analysis, the methodologies used in 2025, emerging trends, and how companies can implement an effective threat analysis framework to safeguard their digital assets. What is Cyber Security Threat Analysis? Cyber Security Threat Analysis is the process of identifying, assessing, and prioritizing potential and existing cybersecurity threats. It involves analyzing data from various sources to uncover vulnerabilities, detect malicious activity, and evaluate the potential impact on systems, networks, and data. The goal is to proactively defend against attacks rather than react to them after damage is done. Why Threat Analysis Matters in 2025 With the growing adoption of AI, IoT, cloud computing, and remote work, the digital landscape has expanded. This has also widened the attack surface for threat actors. According to recent studies, global cybercrime costs are projected to reach $10.5 trillion annually by 2025. Threat analysis is no longer optional; it’s a critical component of enterprise cybersecurity strategies. Key Components of Cyber Security Threat Analysis Threat Intelligence Gathering Collecting data from open-source intelligence (OSINT), internal systems, dark web monitoring, and threat intelligence platforms.Threat Identification Recognizing indicators of compromise (IOCs), such as malicious IP addresses, abnormal behavior, and unusual login attempts.Risk Assessment Evaluating the likelihood and potential impact of a threat on business operations.Vulnerability Management Identifying weaknesses in systems, applications, and networks that could be exploited.Incident Response Planning Developing action plans to quickly contain and remediate threats. Types of Cyber Threats in 2025 Threat actors continue to evolve, leveraging advanced techniques to breach even the most secure environments. Here are the most prominent threats organizations face in 2025: Ransomware-as-a-Service (RaaS): Cybercriminals offer ransomware toolkits to affiliates, enabling less-skilled attackers to launch sophisticated attacks. Phishing 3.0: AI-generated deepfake emails and voice messages make phishing harder to detect. Supply Chain Attacks: Attackers compromise third-party software or vendors to gain access to larger networks. Cloud Security Breaches: Misconfigured cloud environments remain a top vulnerability. IoT Exploits: Devices with weak security protocols are targeted to infiltrate larger systems. Insider Threats: Employees or contractors may intentionally or unintentionally expose systems to risk. Modern Threat Analysis Methodologies 1. MITRE ATT&CK Framework The MITRE ATT&CK framework maps the behavior and techniques of attackers, providing a structured method to analyze and predict threats. 2. Kill Chain Analysis Developed by Lockheed Martin, this method breaks down the stages of a cyberattack from reconnaissance to actions on objectives, allowing analysts to disrupt attacks early in the chain. 3. Threat Modeling Threat modeling involves identifying assets, understanding potential threats, and designing countermeasures. STRIDE (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege) is a popular model used in 2025. 4. Behavior Analytics User and Entity Behavior Analytics (UEBA) uses machine learning to detect anomalies in user behavior that could indicate threats. The Role of AI and Automation in Threat Analysis Artificial Intelligence (AI) and automation are revolutionizing Cyber Security Threat Analysis in 2025. AI-driven analytics tools can: Correlate large volumes of data in real-time Detect zero-day vulnerabilities Predict attack patterns Automate incident response processes Platforms like IBM QRadar, Microsoft Sentinel, and Splunk integrate AI capabilities for enhanced threat detection and response. Building a Threat Analysis Framework in Your Organization Establish Objectives Define what you want to protect, the types of threats to look for, and the goals of your analysis.Choose the Right Tools Invest in threat intelligence platforms, SIEM systems, and endpoint detection and response (EDR) tools.Create a Skilled Team Assemble cybersecurity professionals including threat hunters, analysts, and incident responders.Integrate Data Sources Pull data from internal logs, external intelligence feeds, user activity, and cloud services.Run Simulations Regularly test your threat detection capabilities using red teaming and penetration testing.Review and Adapt Continuously update the threat model based on evolving threats and organizational changes. Metrics to Measure Threat Analysis Success Mean Time to Detect (MTTD): Time taken to identify a threat. Mean Time to Respond (MTTR): Time taken to neutralize the threat. False Positive Rate: Accuracy of alerts generated. Threat Coverage: Percentage of known threats the system can detect. Business Impact Score: How much value the threat analysis process adds to business continuity and risk mitigation. Challenges in Cyber Security Threat Analysis Data Overload: Managing and analyzing massive volumes of data can be overwhelming. Alert Fatigue: Too many alerts, including false positives, reduce response effectiveness. Talent Shortage: Skilled cybersecurity professionals are in high demand but short supply. Rapid Threat Evolution: Attack techniques evolve quickly, making it hard to maintain up-to-date defenses. Best Practices for Effective Threat Analysis Prioritize Critical Assets: Focus analysis efforts on high-value systems and data. Implement Zero Trust Security: Never trust, always verify; ensure robust identity and access controls. Automate Where Possible: Use automation to handle repetitive tasks and free up human resources for strategic analysis. Encourage a Security Culture: Train employees to recognize and report suspicious activity. Leverage Community Intelligence: Participate in threat intelligence sharing communities like ISACs (Information Sharing and Analysis Centers). Future of Threat Analysis Beyond 2025 The future of Cyber Security Threat Analysis will continue to evolve with: Quantum Computing Threats: New cryptographic challenges will require upgraded threat models. Decentralized Threat Intelligence: Blockchain-based threat sharing platforms could emerge. Autonomous Cyber Defense: AI systems capable of defending networks without human input. Conclusion Cyber Security Threat Analysis is an indispensable element of modern digital defense, especially in a hyper-connected 2025. With increasingly sophisticated threats on the horizon, businesses must adopt proactive threat analysis strategies to protect their digital environments. From leveraging AI tools to integrating structured methodologies like MITRE ATT&CK and STRIDE, a multi-layered approach can provide robust defense against cyber adversaries. Investing in skilled teams, up-to-date technologies, and continuous improvement is essential to building resilient cybersecurity infrastructure. FAQs 1. What is Cyber Security Threat Analysis? It is the process of identifying, evaluating, and mitigating potential cybersecurity threats to protect data, networks, and systems.2. Why is threat analysis important in 2025? With rising digital threats and complex attack vectors, proactive analysis helps businesses prevent breaches and minimize damage.3. Which tools are best for threat analysis? Popular tools include Splunk, IBM QRadar, Microsoft Sentinel, and CrowdStrike.4. How does AI help in threat analysis? AI helps by automating data analysis, detecting patterns, and predicting threats in real-time.5. What industries benefit most from threat analysis? Finance, healthcare, government, and tech sectors, where data protection and regulatory compliance are critical.Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

Posted on : May 28, 2025 By Tech World Times Business  Rate this post Starting a new business is exciting. But money is often the first big hurdle. Without funds, it’s hard to build products, hire staff, or run ads. That’s where Startup Funding becomes important. It helps entrepreneurs turn ideas into real businesses. In this article, we share the top 10 startup funding sources for new entrepreneurs in 2025. 1. Personal Savings Most founders start with their own money. It’s simple and quick. You don’t owe anyone else. Using savings shows you believe in your idea. But only use what you can afford to lose. Avoid risking your rent or emergency funds. 2. Friends and Family This is a common early funding source. People who know and trust you may help. They might give you money or offer loans. But always keep it professional. Write down terms and make repayment plans clear. It avoids confusion and protects relationships. 3. Angel Investors Angel investors are wealthy individuals who support startups. They often invest in early stages of business. They bring both money and advice. They usually take equity in return. Search platforms like AngelList or attend startup events to meet them. 4. Venture Capital (VC) VC firms invest big money in fast-growing startups. They look for high returns and innovation. VC funding is best for tech or scalable startups. They take equity and sometimes want a say. It’s competitive, so prepare strong pitch decks. 5. Business Incubators and Accelerators These programs help startups grow fast. They offer funding, training, and mentorship. Some popular examples are Y Combinator and Techstars. They often end with demo days to attract investors. You may give up a small equity share. 6. Crowdfunding Crowdfunding is when many people fund your idea online. Sites like Kickstarter and Indiegogo make this easy. You offer early access or rewards instead of equity. It works well for products people can see. Be sure to promote your campaign heavily. 7. Bank Loans Banks offer loans for startups and small businesses. They usually require a credit check and a business plan. Interest rates vary. Some banks need collateral. If approved, loans give quick access to funds. This option doesn’t dilute your ownership. 8. Government Grants Many governments support small businesses. They offer grants for innovation, research, or job creation. Grants don’t require repayment or equity. But they involve paperwork and clear guidelines. Search local government or small business websites. 9. Business Competitions Pitch competitions can offer funding and exposure. Entrepreneurs present their ideas to a panel of judges. Winners receive cash prizes or investment offers. Even if you don’t win, you gain feedback. Search for startup contests in your city or industry. 10. Corporate Venture Funds Big companies often invest in small startups. They want access to new ideas and technologies. These corporate funds work like VCs. But they may also offer partnerships or clients. Look for companies related to your industry. Tips for Choosing the Right Source Choosing the right Startup Funding source is important. Think about your business stage and goals. Do you need quick cash or long-term help? Can you give up equity or not? Always read the terms and plan your pitch. How to Get Ready for Funding Before applying for funding, get prepared. Make a clear business plan and pitch deck. Know your numbers: costs, sales, profits, and growth plans. Show why your idea is different or better. Practice your pitch until you feel confident. Pros and Cons of Funding Options Each Startup Funding source has pros and cons. Here’s a quick comparison for easy understanding: SourceProsConsSavingsFull controlRisk of personal lossFriends/FamilyEasy accessCan hurt relationshipsAngelsSmart moneyGive up equityVCsLarge fundsHigh pressure to growIncubatorsSupportiveEquity shareCrowdfundingNo equity neededNeeds marketingBank LoansKeep full ownershipInterest, credit checksGrantsFree moneySlow and competitiveCompetitionsWin fundingNo guaranteeCorporate FundsBig supportMight want exclusivity Choose what fits your situation best. Final Thoughts Finding Startup Funding takes effort, but it’s possible. Start small, and build trust with each step. Many successful companies began with tiny investments. You don’t need millions to get started. Pick the right funding source and stay focused. With patience and planning, your startup can grow. Keep learning, keep networking, and never stop pitching. Your big break might be one meeting away. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

AI could soon surpass Bitcoin mining in energy consumption, according to a new analysis that concludes artificial intelligence could use close to half of all the electricity consumed by data centers globally by the end of 2025.The estimates come from Alex de Vries-Gao, a PhD candidate at Vrije Universiteit Amsterdam Institute for Environmental Studies who has tracked cryptocurrencies’ electricity consumption and environmental impact in previous research and on his website Digiconomist. He published his latest commentary on AI’s growing electricity demand last week in the journal Joule. AI already accounts for up to a fifth of the electricity that data centers use, according to de Vries-Gao. It’s a tricky number to pin down without big tech companies sharing data specifically on how much energy their AI models consume. De Vries-Gao had to make projections based on the supply chain for specialized computer chips used for AI. He and other researchers trying to understand AI’s energy consumption have found, however, that its appetite is growing despite efficiency gains — and at a fast enough clip to warrant more scrutiny.“Oh boy, here we go.”With alternative cryptocurrencies to Bitcoin — namely Ethereum — moving to less energy-intensive technologies, de Vries-Gao says he figured he was about to hang up his hat. And then “ChatGPT happened,” he tells The Verge. “I was like, Oh boy, here we go. This is another usually energy-intensive technology, especially in extremely competitive markets.” There are a couple key parallels he sees. First is a mindset of “bigger is better.” “We see these big tech [companies] constantly boosting the size of their models, trying to have the very best model out there, but in the meanwhile, of course, also boosting the resource demands of those models,” he says. That chase has led to a boom in new data centers for AI, particularly in the US, where there are more data centers than in any other country. Energy companies plan to build out new gas-fired power plants and nuclear reactors to meet growing electricity demand from AI. Sudden spikes in electricity demand can stress power grids and derail efforts to switch to cleaner sources of energy, problems similarly posed by new crypto mines that are essentially like data centers used to validate blockchain transactions. The other parallel de Vries-Gao sees with his previous work on crypto mining is how hard it can be to suss out how much energy these technologies are actually using and their environmental impact. To be sure, many major tech companies developing AI tools have set climate goals and include their greenhouse gas emissions in annual sustainability reports. That’s how we know that both Google’s and Microsoft’s carbon footprints have grown in recent years as they focus on AI. But companies usually don’t break down the data to show what’s attributable to AI specifically.To figure this out, de Vries-Gao used what he calls a “triangulation” technique. He turned to publicly available device details, analyst estimates, and companies’ earnings calls to estimate hardware production for AI and how much energy that hardware will likely use. Taiwan Semiconductor Manufacturing Company (TSMC), which fabricates AI chips for other companies including Nvidia and AMD, saw its production capacity for packaged chips used for AI more than double between 2023 and 2024. After calculating how much specialized AI equipment can be produced, de Vries-Gao compared that to information about how much electricity these devices consume. Last year, they likely burned through as much electricity as de Vries-Gao’s home country of the Netherlands, he found. He expects that number to grow closer to a country as large as the UK by the end of 2025, with power demand for AI reaching 23GW. Last week, a separate report from consulting firm ICF forecast a 25 percent rise in electricity demand in the US by the end of the decade thanks in large part to AI, traditional data centers, and Bitcoin mining. It’s still really hard to make blanket predictions about AI’s energy consumption and the resulting environmental impact — a point laid out clearly in a deeply reported article published in MIT Technology Review last week with support from the Tarbell Center for AI Journalism. A person using AI tools to promote a fundraiser might create nearly twice as much carbon pollution if their queries were answered by data centers in West Virginia than in California, as an example. Energy intensity and emissions depend on a range of factors including the types of queries made, the size of the models answering those queries, and the share of renewables and fossil fuels on the local power grid feeding the data center. It’s a mystery that could be solved if tech companies were more transparentIt’s a mystery that could be solved if tech companies were more transparent about AI in their sustainability reporting. “The crazy amount of steps that you have to go through to be able to put any number at all on this, I think this is really absurd,” de Vries-Gao says. “It shouldn’t be this ridiculously hard. But sadly, it is.”Looking further into the future, there’s even more uncertainty when it comes to whether energy efficiency gains will eventually flatten out electricity demand. DeepSeek made a splash earlier this year when it said that its AI model could use a fraction of the electricity that Meta’s Llama 3.1 model does — raising questions about whether tech companies really need to be such energy hogs in order to make advances in AI. The question is whether they’ll prioritize building more efficient models and abandon the “bigger is better” approach of simply throwing more data and computing power at their AI ambitions. When Ethereum transitioned to a far more energy efficient strategy for validating transactions than Bitcoin mining, its electricity consumption suddenly dropped by 99.988 percent. Environmental advocates have pressured other blockchain networks to follow suit. But others — namely Bitcoin miners — are reluctant to abandon investments they’ve already made in existing hardware (nor give up other ideological arguments for sticking with old habits). There’s also the risk of Jevons paradox with AI, that more efficient models will still gobble up increasing amounts of electricity because people just start to use the technology more. Either way, it’ll be hard to manage the issue without measuring it first. See More: