Adversarial Machine Learning in Practice

In today's rapidly evolving AI landscape, security is no longer optional-it is mission-critical. AI Security Mastery: Introduction to AI Security takes readers inside the world of adversarial machine learning, data poisoning, model theft, and LLM vulnerabilities, showing exactly how attackers exploit modern systems-and how defenders can stay one step ahead.

Designed for machine learning practitioners, engineers, and security professionals, this hands-on guide balances theory with practice. Through clear explanations, real-world case studies, and fully working code examples, you'll learn how to protect ML pipelines from evasion attacks, detect backdoor triggers, defend against membership inference, and operationalize MLSecOps with continuous adversarial testing. Step-by-step labs using IBM ART, CleverHans, and MITRE ATLAS provide a practical foundation, making complex threats approachable for beginners while still offering depth for experienced professionals.

What sets this book apart is its focus on real threats, real tools, and real defenses. You won't just read about AI risks-you will build and test them yourself. From securing LLMs against prompt injection to deploying monitoring pipelines that catch anomalies in real time, this book delivers actionable techniques that can be applied immediately in industry settings.

Written by Calvin Dolton, a recognized voice in AI security and applied machine learning, the AI Security Mastery series bridges the gap between cutting-edge research and practical engineering. Dolton's approachable style and emphasis on reproducible labs ensure that readers not only understand the concepts but can implement them with confidence.

Whether you're a data scientist, security engineer, or technology leader, this book equips you with the knowledge to secure AI systems in 2025 and beyond. In a world where attackers move fast and AI adoption is accelerating, AI Security Mastery ensures you're not left behind