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We're back—and completely different.#2: CYBER_AI and the BasicsWe're back—and completely different.Welcome to CYBER_AI, a new newsletter from the Packt team focusing on—well, exactly what it says on the tin: cybersecurity in the age of AI.This time, we're taking a bold step: a step into the world of teaching in a world where we're all still figuring it out. A scary prospect, to say the very least. As we are beginning this newsletter from the ground up, we are taking things all the way back to the very simplest questions that crop up when we ask "what is Cyber AI?" For a look into the future, here's our plan:1. What “Cybersecurity AI” Actually Means2. Machine Learning 101 for Security Professionals3. Threat Detection with AI: From Rules to Models4. Adversarial Machine Learning Basics5. LLMs in Cybersecurity: Capabilities and Limitations6. Securing AI Models and Pipelines (AI Supply Chain Security)7. AI-Enhanced Offensive Techniques8. Privacy and Data Protection in AI Systems9. AI Governance, Ethics, and Risk Management10. Building a Security-Aware AI WorkflowSound good? Head over to Substack and sign up there!Join us on Substack to find our bonus articles!In this newsletter, we’ll explore how AI is transforming cybersecurity—what’s new, what’s next, and what you can do to stay secure in the age of intelligent threats.Welcome aboard! The future of cyber defence starts here.Cheers!Austin MillerEditor-in-ChiefLLMs and Agentic AI In Production - Nexus 2025Build and fine-tune your own LLMs and Agents and deploy them in production with workshops on MCP, A2A, Context Engineering, and many more.Book now at 50% off with the code CYBER50News WipeDisrupting the first reported AI-orchestrated cyber espionage campaign: "We recently argued that an inflection point had been reached in cybersecurity: a point at which AI models had become genuinely useful for cybersecurity operations, both for good and for ill. This was based on systematic evaluations showing cyber capabilities doubling in six months; we’d also been tracking real-world cyberattacks, observing how malicious actors were using AI capabilities. While we predicted these capabilities would continue to evolve, what has stood out to us is how quickly they have done so at scale."Hard drives on backorder for two years as AI data centers trigger HDD shortage — delays forcing rapid transition to QLC SSDs: The race to achieve AGI (artificial general intelligence) has pushed constituents to invest in and build data centers at a pace far outstripping our ability to make them. Manufacturers are struggling to keep up with AI demand, and theongoing DRAM shortageis proof of this, with memory kits costing more than double what they did just a few months ago. Now,DigiTimesis reporting that storage is taking a hit, too, with delivery times forenterprise-grade HDDs delayed by two years.Prompt Injection in AI Browsers:This is why AIs are not ready to be personal assistants: A new attack called ‘CometJacking’ exploits URL parameters to pass to Perplexity’s Comet AI browser hidden instructions that allow access to sensitive data from connected services, like email and calendar. In a realistic scenario, no credentials or user interaction are required and a threat actor can leverage the attack by simply exposing a maliciously crafted URL to targeted users.Culture, You, and AIIntroducing SlopStop: Community-driven AI slop detection in Kagi Search: "AI slop is deceptive or low-value AI-generated content, created to manipulate ranking or attention rather than help the reader. Per ourAI integration philosophy, we’re not against AI tools that enhance human creativity. But when it includes fake reviews, fabricated expertise, misinformation, content farms designed purely for profit rather than value, and systems that seek to replace genuine human insight and connection, we know it’s hurting us, and we take it upon ourselves to act."Can we bootstrap AI Safety despite being unable to even define it?: Many approaches to AI safety rely on inspecting model outputs or activations, yet certain risks are inherently undetectable by inspection alone. We propose a complementary, architecture-agnostic approach that enhances safety through the aggregation of multiple generative models, with the aggregated model inheriting its safety from the safest subset of a given size among them.Automating Algorithm Discovery: A Case Study in Transaction Scheduling: "Transactions are a cornerstone of modern data stores, offering guarantees that simplify development under concurrent data access for modern applications. Their wide adoption by industry databases highlights their importance. However, modern applications pose new hurdles for supporting these semantics. Driven by massive data growth and new use cases such as AI agents, storage systems face unprecedented data volume and scale."From the cutting edge“Generative AI revolution in cybersecurity: a comprehensive review of threat intelligence and operations”: This paper surveys how generative AI (GenAI) is transforming cybersecurity — especially in threat intelligence, automated operations, and attack simulation. It discusses how generative models can be used defensively (e.g., synthesizing threat data, automating incident response) but also warns of adversarial use. The review covers existing architectures, use-cases, risks, and future research directions.“Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms”: This is a broad, in-depth survey of AI and ML techniques applied to key cybersecurity areas like intrusion detection, malware classification, behavior analysis, and threat intelligence. It also outlines future paradigms, challenges (e.g., adversarial ML, explainability), and research gaps.“Towards Explainable and Lightweight AI for Real-Time Cyber Threat Hunting in Edge Networks” (Milad Rahmati): This is a preprint (arXiv) proposing an AI framework for detecting cyber threats in edge networks (i.e., resource-constrained devices). The core idea is to combine interpretable machine learning (e.g., decision trees) with lightweight deep learning and federated learning, to achieve real-time threat hunting while preserving transparency and low computational cost.“Adaptive Cybersecurity: Dynamically Retrainable Firewalls for Real-Time Network Protection” (Sina Ahmadi): Another preprint. This work introduces the concept of dynamically retrainable firewalls — firewall systems that use continual or reinforcement learning to retrain in real time as network traffic evolves. It discusses the architecture, latency/resource tradeoffs, integration with zero-trust models, and future risks including adversarial attacks and ethical/regulatory concerns.“Organizational Adaptation to Generative AI in Cybersecurity: A Systematic Review” (Christopher Nott): This systematic review studies how cybersecurity organizations (e.g., in finance, critical infrastructure) are restructuring processes and governance to integrate generative AI. It identifies patterns such as LLM integration for threat modeling, risk automation, and hybrid human–AI operations. It also explores challenges: data quality, explainability, adversarial attacks, and building governance frameworks.“A cybersecurity AI agent selection and decision support framework” (Masike Malatji): This recent preprint proposes a framework to help organizations choose what kind of AI “agent” (reactive, cognitive, hybrid, learning) to deploy, aligned with the NIST Cybersecurity Framework (CSF 2.0). It maps properties like autonomy, learning, and responsiveness to NIST CSF functions. It recommends different autonomy levels (assisted, augmented, fully autonomous) depending on maturity and risk.*{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}}

A quick guide to AI agents, a must-grab $18 bundle, and a hands-on trading workshop.Subscribe|Submit a tip|Advertise with Us💡 Welcome to BIPro #118Your weekly signal on analytics, automation, and the evolution of business intelligence.This week, we spotlight Sagar Lad, a Data & AI Solution Architect with a knack for turning abstract AI concepts into clear, applied insights. In his latest piece, “What Are AI Agents? A Simple Guide,” Sagar explores how intelligent agents are moving beyond static automation, transforming data-driven systems into adaptive, goal-oriented entities.From customer service bots that learn through interaction to autonomous systems that optimize decisions in real time, Sagar breaks down the internal logic of AI agents: how they perceive data, reason across states, and act with context awareness. It’s a concise, structured read that bridges AI theory with practical BI applications, offering a glimpse into how agent-based intelligence will reshape workflows, analytics, and decision automation.📘 Deal of the Week: Packt’s Humble BundleSharpen your analytical edge with 3 best-selling Packt books for just $18. This curated Humble Bundle is built for data professionals looking to master applied Python and analytics, featuring Python Machine Learning by Example, Causal Inference and Discovery in Python, and Modern Time Series Forecasting with Python. Over 2,600 bundles sold and $4,500 raised for charity so far, don’t miss your chance to grab 25 essential data and BI books for a fraction of the price while supporting Direct Relief.📈 Workshop Spotlight: Algorithmic Trading with Python (Cohort 2)On December 12, join Jason Strimpel for a hands-on workshop on building algorithmic trading systems with Python, pandas, VectorBT, and Interactive Brokers API. Explore how data-driven strategy design, backtesting, and real-time execution come together, an ideal session for BI professionals looking to extend their analytical skills into financial modeling and automation.Let’s dive in 👇Cheers,Merlyn ShelleyGrowth Lead, PacktWhat Are AI Agents? A Simple Guide by Sagar LadArtificial Intelligence (AI) is transforming the way we live and work. One of the most exciting developments in this field is the rise ofAI agents. But what exactly are they? Let’s break it down in a way that’s easy to understand.What Is an AI Agent?AnAI agentis a computer program that canperceive its environment,make decisions, andtake actionsto achieve a goal. Think of it like a digital assistant that doesn’t just follow instructions — it learns, adapts, and acts intelligently.Task Execution Flow by AI AgentsExample:Imagine a robot vacuum cleaner. It senses where dirt is, avoids obstacles, and decides the best path to clean your room. That’s an AI agent in action!Internal Working of an AI Agent1. Perception (Input Layer)The agent receives data from its environment.This could be sensor data (for robots), user input (for chatbots), or digital signals (for trading bots).Internally, this data is processed into a format the agent can understand often as structured data or features.Example:A self-driving car uses cameras and sensors to detect pedestrians, traffic lights, and road signs.2. Knowledge Base / State RepresentationThe agent maintains a model of the world or its environment.This could be a simple memory of past actions or a complex map of the surroundings.It helps the agent understand context and make informed decisions.Example:A chess-playing agent remembers the current board state and possible moves.3. Decision-Making (Reasoning Engine)Based on the perceived data and internal state, the agent decides what to do next.This can involve:Rule-based logic(if-then rules)Search algorithms(finding the best path or solution)Machine learning models(predicting outcomes)Reinforcement learning(learning from trial and error)Example:A recommendation agent uses a machine learning model to suggest movies based on your viewing history.4. Action (Output Layer)The agent performs an action in the environment.This could be sending a message, moving a robot arm, or updating a database.Example:A chatbot replies to a user query with a helpful answer.5. LearningSome agents improve over time by learning from feedback or new data.Supervised learning(learning from labeled data)Unsupervised learning(finding patterns)Reinforcement learning(learning from rewards and penalties)Example:A game-playing agent gets better by playing thousands of games and learning which strategies win.🔄 The Agent LoopHere’s a simplified loop that most AI agents follow:Perceive → Analyze → Decide → Act → Learn (optional) → Repeat👉 Continue reading the full article on the Packt Medium handle.📘 Why You Shouldn’t Miss This Humble BundleIf you’re looking to strengthen your data and analytics skills, this is an opportunity worth taking. Packt’s Modern Data Analytics & Business Intelligence Mastery Bundle brings together 25 carefully selected titles from trusted experts in data, analytics, and AI, and you can start with 3 bestsellers for just $18.Inside, you’ll find practical guides like Python Machine Learning by Example, Causal Inference and Discovery in Python, and Modern Time Series Forecasting with Python. These books go beyond theory and help you apply what you learn to real-world challenges. Whether you’re building dashboards, experimenting with predictive models, or mastering Power BI and Microsoft Fabric, this bundle provides the hands-on knowledge you need to stay ahead in an evolving field.Over 2,600 bundles sold and $4,500 raised for charity so far shows the impact this collection is already making. When you buy, you’re not only investing in your growth but also supporting Direct Relief.It’s a valuable way to learn, grow, and give back, available for a limited time.👉 Grab your bundle before it’s gone!Grow your skills while supporting a great causeSee you next time!*{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}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @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}}