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Exploring Unit 42's findingsDon't miss out!Sign up today!#221: Digging into Social Engineering, part 1Welcome to another_secpro!This week, we're poking the brain of CISO expert David Gee to deliver you some insights which line up nicely with his new book: A Day in the Life of a CISO. We've also included our popular PDF resource again, to help you improve your training sessions and help the non-specialists amongst us to make the right moves in the age of AI. Check it out!Check out _secpro premiumIf you want more, you know what you need to do: sign up to the premium and get access to everything we have on offer. Click the link above to visit our Substack and sign up there!Cheers!Austin MillerEditor-in-ChiefThis week's articleUnit 42 on non-phishing vectorsRecently, along with a wealth of other industry-critical information and resources, Palo Alto’s Unit 42 published their incident response report concerning social engineering. As an area of practice that has always fascinated me—as more art than science—this immediately grabbed my attention and almost forced me to start taking notes. With this in mind, we as a team are heading out over the next few weeks to dig deeper into social engineering and help you discern the golden kernels that you need to access.Check it out todayNews BytesUnit 42 Threat Bulletin – October 2025: Published 21 October 2025, this monthly bulletin by Unit 42 (the threat-research arm of Palo Alto Networks) surfaces multiple emerging threats. Highlights include the self-propagating supply-chain worm “Shai-Hulud”, an advanced supply-chain attack targeting npm packages; detailed technical IOCs; and spotting a new Chinese-nexus APT “Phantom Taurus” targeting government/telecom across Africa/Middle East/Asia.PacketWatch Cyber Threat Intelligence Report: Crafted by Intelligence Team and published 20 October 2025, this bi-weekly briefing highlights: (a) the major breach incident at F5 Networks (source code + undisclosed vulnerabilities); (b) a list of critical and high-severity vulnerabilities across major platforms (Oracle, Microsoft, Veeam, SAP, 7-Zip, Ivanti); and (c) a renewed emphasis on user-targeted attacks such as credential phishing, fake CAPTCHA software, and fake downloads.Disrupting malicious uses of AI(PDF): Released by OpenAI, this October 2025 update (PDF) details how threat actors are increasingly leveraging multiple AI tools (e.g., using one model for planning and another for execution), integrating AI into existing cyber-attack workflows, rather than inventing wholly new attack methods. The report also gives case studies of misuse (scams, code-signing abuse, social engineering) and how defence and detection are adapting.Microsoft Digital Defense Report 2025: Lighting the path to a secure future(PDF):Published by Microsoft 21 October 2025, this annual-style report provides their threat intelligence view: major uptick in AI-enabled adversary operations, increasing geopolitical cyber-conflict, supply chain risk, and the imperative for defenders to rethink traditional security models given the speed and scale of modern attacksENISA Threat Landscape 2025 (PDF):Published 7 October 2025 by ENISA (European Union Agency for Cyber Security), this comprehensive PDF analyses 4,875 incidents (1 July 2024–31 June 2025) to map global threat trends: shift toward mixed/campaign-style operations, AI-enabled threat activity, supply chain convergence, and increased adversary speed. Though slightly earlier than your window, its release date is timely and gives context for many of the current week’s incidents.This week's academiaFrom Texts to Shields: Convergence of Large Language Models and Cybersecurity (Tao Li, Ya-Ting Yang, Yunian Pan & Quanyan Zhu):This paper explores how large language models (LLMs) are increasingly converging with cybersecurity tasks: for example, using LLMs for vulnerability analysis, network and software security tasks, 5G-vulnerability assessment, generative security engineering and automated reasoning in defence scenarios. The authors highlight socio-technical challenges (trust, transparency, human-in-the-loop, interpretability) when deploying LLMs in high-stakes security settings, and propose a forward-looking research agenda to integrate formal methods, human-centred design and organisational policy in LLM-enhanced cyber-operations.Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation (Tharcisse Ndayipfukamiye, Jianguo Ding, Doreen Sebastian Sarwatt, Adamu Gaston Philipo & Huansheng Ning): This survey conducts a PRISMA-style review (2021–Aug 2025) of how Generative Adversarial Networks (GANs) are being used both as attack tools and defensive tools in cybersecurity. They analyse 185 peer-reviewed studies, develop a taxonomy across four dimensions (defensive function, GAN architecture, cybersecurity domain, adversarial threat model), and identify key gaps: training instability, lack of standard benchmarks, high computational cost, limited explainability. They propose a roadmap towards scalable, trustworthy GAN-powered defences.Securing the AI Frontier: Urgent Ethical and Regulatory Imperatives for AI-Driven Cybersecurity (Vikram Kulothungan): This article examines the ethical and regulatory challenges arising from the deployment of AI in cybersecurity. It traces historical regulation of AI, analyses current global frameworks (e.g., the EU AI Act), and discusses key issues including bias, transparency, accountability, privacy, human oversight. The paper proposes strategies for enhancing AI literacy, public engagement, and global harmonisation of regulation in AI-driven cyber-systems.A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems (Benyamin Tafreshian & Shengzhi Zhang):The authors propose a multi-layer defensive framework aimed at ML‐based Network Intrusion Detection Systems (NIDS) which are vulnerable to adversarial evasion. Their framework integrates adversarial training, dataset balancing, advanced feature engineering, ensemble learning, and fine-tuning. On benchmark datasets NSL-KDD and UNSW-NB15, they report on average a ~35% increase in detection accuracy and ~12.5% reduction in false positives under adversarial conditions.Cyber Security: State of the Art, Challenges and Future (W.S. Admass et al.): This article presents an overview of the state of the art in cybersecurity: existing architectures, key challenges, and emerging trends globally. It reviews tactics, techniques, and procedures (TTPs), current defence mechanisms and future research directions.DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks (Jing Chen, Onat Güngör, Zhengli Shang, Elvin Li & Tajana Rosing): This paper introduces “DYNAMITE”, a framework for dynamically selecting the optimal defence mechanism for ML-based Intrusion Detection Systems (IDS) when under adversarial attack. Instead of applying a static defence, DYNAMITE uses a meta-ML selection mechanism to pick the best defence in real-time, reducing computational overhead by ~96.2% compared to an oracle and improving F1-score by ~76.7% over random defence and ~65.8% over the best static defence.*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Exclusive Invite: Packt’s Nexus 2025 – The Global Agentic AI Event. AI_Distilled #119: What’s New in AI This Week It’sbeen a week of recalibration across the AI landscape:billion-dollar copyright reckonings,tightening global regulations,layoffs, lawsuits, and bold experiments redefining what “AI-powered” really means. Underneath the noise, a pattern is emerging: the industry is shifting from rapid expansion to structural accountability. Whether it’s Anthropic’s landmark settlement, China’s new AI governance laws, or SAP’s methodical rollout of enterprise agents, the message is clear "AI’s next phase is about stewardship rather than scale." Dive into this week’s curation for the full picture! LLM Expert Insights, Packt EXPERT INSIGHTS Building Trustworthy Intelligence: The Road to Responsible AI in LLMs In this week’s feature, Ahmed Menshawy and Mahmoud Fahmy, authors of LLMs in Enterprise, unpack how organizations can balance innovation with responsibility when deploying large language models. They outline the four pillars of Responsible AI (RAI):fairness, transparency, accountability, and safety, as the foundation for building trustworthy systems. From bias detection and explainability tools to continuous compliance and regulatory alignment, the article shows how ethics becomes engineering through practical frameworks and real-world safeguards. As global standards like the EU AI Act and NIST RMF tighten accountability, RAI isn’t just good practice; it’s a business imperative. Read the full article on Substack → Special Message from Packt's Events Team: This November, the world’s top AI Experts from Google, Microsoft, and LangChain are coming together for Packt's Nexus 2025, a two-day live virtual summit for developers, engineers, and AI practitioners ready to build the next generation of intelligent systems. Join the Experts Redefining AI | Live at Nexus 2025. BOOK YOUR SEAT NOW! Use code: EARLY50 to get 50% discount on the ticket - Exclusive for the AI_Distilled Community 📈LATEST DEVELOPMENT OpenAI launches AI browser that can browse and act for you What happened: OpenAI introducedChatGPT Atlas, a Chromium-based browser with the ChatGPT assistant built in. It currently supports macOS and uses features like a sidebar for summarising websites, indexing your browsing history, and an “Agent Mode” that enables the AI to perform tasks like shopping and tab management, all with optional privacy modes for logged-out usage. Why it matters: By integrating LLMs directly into the browser, OpenAI is shifting how we access and interact with the web from manual searches to conversational and action-based interfaces. This move also elevates questions of privacy, data control, and the evolving role of browsers as AI-enabled platforms. (Tom’s Hardware) DeepSeek explores AI efficiency with token-to-image compression What happened: Chinese startupDeepSeek unveiled a new model that converts text tokens into images using a vision encoder, a technique that could overcome the “long-context” limits of LLMs. The model, called DeepSeek-OCR, compresses text inputs up to 10× whilemaintainingabout97 %accuracy, sparking discussion across the global AI community. Why it matters: This research could pave the way for LLMs that handle far longer prompts and reasoning chains without massive computational costs. If successful, it would mark a breakthrough in scaling efficiency, one of the biggest challenges in current AI architectures. (South China Morning Post) Anthropic to pay$1.5 billionin landmark copyright settlement What happened: Anthropic has agreed to pay$1.5 billion to authors after using their copyrighted books scraped from sites like LibGen and PiLiMi to train its Claude models without permission. Around half a million authors are eligible for compensation, and Anthropic must also destroy all pirated copies. Why it matters: The settlement sets a precedent for how AI companies handle copyrighted data, signaling that unlicensed use of creative works now carries real financial risk. It may also push the industry toward formal licensing deals between publishers and AI developers.(Chemistry World) China strengthens AI oversight with new data and safety laws What happened: China’s top legislature is drafting amendments to its cybersecurity law to include stricterAI safety, ethics, and data protectionmeasures. The proposed framework supports AI research while tightening oversight of generative models and content labeling, including mandatory visible and hidden identifiers for AI-generated media. Why it matters: The move signals Beijing’s intent to balance AI growth with tighter governance, aiming to prevent misinformation and data misuse. It also highlights a divergence from U.S. policy; China’s focus is regulation-first, while American firms emphasize commercial deployment. (Business Standard) Study warns of ‘brain rot’ in AI models trained on junk web data What happened: A study by researchers fromTexasA&M, the University of Texas at Austin, and Purdue University found that large language models suffer “cognitive decline” when repeatedly trained on low-quality, engagement-driven content. The paper titled LLMs Can Get Brain Rot! shows that reasoning accuracy in tested models droppednearly20points, and long-context comprehension fell over30 pointswhen fed junk social media data.(Business Standard) Why it matters: The findings underline thatdata quality directly affects AI reliability and ethics, not just performance. Models exposed to “viral” or superficial web textexhibitedreasoning shortcuts, overconfidence, and personality drift—effects researchers call “persistent representational decay.” The paper urges developers to treat data hygiene as acore AI safety issue, recommending cognitive audits and stricter content filtering during training.(arXiv) OpenAI’s South Korea blueprint envisions AI-led economic growth What happened: OpenAI released anEconomic Blueprint for South Korea, outlining policy recommendations to scale AI adoption through partnerships with Samsung, SK, and the Ministry of Science and ICT. The plan builds on OpenAI’sStargate initiative, focused on advanced memory and next-gen data centers, and aims to pair sovereign AI development with frontier collaborations. (OpenAI) Why it matters: South Korea is positioning itself as the nextglobal AI powerhouse,leveragingits semiconductor dominance, digital infrastructure, and government-backed funding. The blueprint calls for AI-led growth inexports, healthcare, education, and SMEs, alongside governance sandboxes and data infrastructure standards, framing Korea as both an adopter and standard-setter in safe, scalable AI deployment.(OpenAI) Dell Technologies Capital bets on AI data and new architectures What happened: Dell Technologies Capital (DTC) managing director Daniel Docter and partner Elana Lian outlined their vision fornext-generation AI architectures and “frontier data”in a Crunchbase interview. Dell expects$20 billionin AI server shipmentsby 2026 and has loggedfive portfolio exits since June, including Meta’s acquisition ofRivosand Salesforce’s acquisition ofRegrello. Why it matters: DTC sees AI’s future as adata problem more than a model problem, backing startups innovating in reasoning, safety, and new architectures such asstate-space modelsfor long-context and voice AI. The firm’s focus spans from silicon to applications, reflecting how enterprise AI is now driven by infrastructure, not hype.(Crunchbase) Google launches Skills platform with 3,000 AI courses What happened: Google unveiledGoogle Skills, a unified learning hub offeringnearly3,000AI and technical coursesfrom Google Cloud, DeepMind, and Grow with Google. The platform features hands-on labs powered by Gemini Code Assist, gamified progress tracking, and credentials ranging from skill badges to professional certificates.(Analytics India Magazine) Why it matters: As demand for AI talent accelerates, Google’s platform could playa central roleinbridging global workforce gaps, especially by offering free access to students, nonprofits, and developers. It emphasizesapplied, hands-on learningrather than passive video courses, signaling how tech giants are retooling education to meet enterprise AI demand.(Analytics India Magazine) Elon Musk says AI will take every job and humans will be free to grow vegetables In his latest comments on X,Elon Muskdeclared that“AI and robots will replace all jobs.” Far from a dystopian warning, Musk argued this shift could liberate humanity from the need to work, likening future labor to an optional hobby such as “growing your own vegetables instead of buying them from the store.” The remark came in response to reports about Amazon’s plan to replace over 160,000 jobs with robots by 2027. While his statement reignited debates about automation anxiety, Musk framed it as an opportunity for universal income and post-labor fulfillment rather than economic ruin.(mint) Build an agent with function calling inGPT-5 Whatyou’lllearn: a practical walk-through of agent design, from defining tool schemas and wiring up function calls to implementing a working web-search agent withTavily, complete with environment setup, code, and a clear loop for handling function outputs vs direct replies. Ifyou’vebeen wanting to move from prompts to real actions, bookmark this and try the tutorial end-to-end:(Towards Data Science) Look beyond LLMs to build the next generation of AI AI veteran Dr. Lance Eliot argues that true progress toward AGI will come from exploring new paradigms from neuro-symbolic and embodied AI to human-centered and quantum approaches rather than scaling today’s language models. If you care about where the next real breakthroughs will emerge, this piece is your roadmap to what comes after generative AI: (Forbes) Built something cool? Tell us. Whether it's a scrappy prototype or a production-grade agent, we want to hear how you're putting generative AI to work. Drop us your story at nimishad@packtpub.com or reply to this email, and you could get featured in an upcoming issue of AI_Distilled. 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️ We would love to know what you thought—your feedback helps us keep leveling up. 👉 Drop your rating here Thanks for reading, The AI_Distilled Team (Curated by humans. Powered by curiosity.) *{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}