Practical AI Security

<b>Break AI Systems. Then Secure Them.</b><br><br>If you’re a security practitioner learning to operate in AI environments, or an ML engineer who needs to understand what adversaries actually do, <i>Practical AI Security</i> gives you the technical foundation the field demands.<br><br>Built from first principles, this book takes you from how models fail to how they’re exploited to how they’re defended and audited. Every technique includes clear explanations and real-world examples, and you can run the attacks and defenses yourself with over 30 hands-on Python demos.<br><br><ul><li>Understand how different kinds of machine learning models create unique vulnerabilities, and explore how these models are integrated into more autonomous, agentic AI systems to introduce new weaknesses and risks.</li><li>Identify, exploit, and defend against dozens of weaknesses and attacks across the AI life cycle, including data poisoning, model theft, and prompt injection.</li><li>Evaluate AI systems for safety failures, bias, and alignment risks using structured benchmarking.</li><li>Threat-model agentic systems, RAG pipelines, and multimodal architectures using MITRE ATLAS, OWASP, and the MAESTRO framework.</li><li>Design and execute AI-specific red teaming campaigns, and understand what makes them distinct from traditional security tests.</li><li>Conduct rapid risk audits and navigate AI governance frameworks for real deployments.</li></ul><br>Whether you use, build, deploy, or oversee AI, this isn’t niche knowledge—it’s the foundation for defending the technologies that will define the next era of human progress.