If you're a security practitioner learning how to work in AI environments, or a machine learning engineer who needs to understand what adversaries are actually doing, Practical AI Security gives you the technical foundation the field requires.Starting from the basic principles, this book takes you on a journey from how models fail, to how to exploit them, to how to protect and revise them. Each technique includes clear explanations and real-world examples, and you can try out the attacks and defenses yourself with more than 30 practical Python demonstrations.
Understand how different types of machine learning models create unique vulnerabilities, and explore how these models can be integrated into more autonomous and effective AI systems to introduce new vulnerabilities and risks.Identify, exploit, and defend against dozens of vulnerabilities and attacks across the AI lifecycle, including data poisoning, model theft, and spot injection.
Evaluate AI systems for safety failures, bias, and compliance risks using structured benchmarks.
Agent systems based on threat models, RAG pipelines, and multimedia architectures using MITER ATLAS, OWASP, and MAESTRO framework.Whether you use, build, deploy, or oversee AI, this is not specialized knowledge — it is the foundation for defending the technologies that will define the next era of human progress.
