Adversarial AI Attacks, Mitigations, and Defense Strategies

Adversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips you with the skills to secure AI technologies, moving beyond research hype or business-as-usual activities.

This strategy-based book is a comprehensive guide to AI security, presenting you with a structured approach with practical examples to identify and counter adversarial attacks. In Part 1, you'll touch on getting started with AI and learn about adversarial attacks, before Parts 2, 3 and 4 move through different adversarial attack methods, exploring how each type of attack is performed and how you can defend your AI system against it. Part 5 is dedicated to introducing secure-by-design AI strategy, including threat modeling and MLSecOps and consolidating recent research, industry standards and taxonomies such as OWASP and NIST. Finally, based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI.

By the end of this book, you'll be able to develop, deploy, and secure AI systems against the threat of adversarial attacks effectively.