Your firewall won't save you from a chatbot that's been talked into leaking customer data.
AI security is a new game with a familiar feel. The attackers are clever, the defenders are catching up, and most of the advice out there is written for people who already speak fluent machine learning. This book is for the rest of us. If you're a security pro crossing over from traditional cyber, a developer wiring an LLM into your product, a leader trying to write a sensible AI policy, or a curious technologist who wants to understand the risks without wading through research papers, you're in the right place.
Inside this book, you'll learn how to:
Recognise the new attack surfaces that come with AI: prompts, training data, model weights, and agent tools
Spot and stop prompt injection, jailbreaks, data poisoning, and model theft before they reach production
Build layered defences with guardrails, content filters, monitoring, and red-team testing
Secure modern AI patterns including agents, tool use, and retrieval-augmented generation
Map your work to NIST AI RMF, ISO/IEC 42001, and the EU AI Act without drowning in acronyms
Write an AI policy your colleagues will actually read, and bring shadow AI in from the cold
Bookmark the right resources so you can keep up as the field shifts under your feet
Written in the friendly, jargon-light style of The Plain-English Guide series, this book treats you like a smart professional who's new to a specific topic, not a beginner who needs to be talked down to. Every new term gets a plain-English definition, every abstract idea gets a real-world analogy, and every chapter ends with a recap so nothing slips away.
Whether you're shipping your first LLM feature or trying to govern a sprawl of AI tools already in your org, this book gives you a clear, friendly path from "I have no idea where to start" to "I've got this."
Open the sample to start reading.
