About this book:

*Adversarial AI and Security* provides a comprehensive framework for understanding and mitigating the unique vulnerabilities of machine learning (ML) systems. The book transitions from a foundational taxonomy of attacks—including data poisoning, backdoors, and evasion—to sophisticated defensive architectures like robust training, certified robustness, and differential privacy. By framing AI security as a lifecycle discipline rather than a series of isolated patches, the text emphasizes that the integrity of an intelligent system depends on the security of its entire supply chain, from raw data ingestion to real-time inference monitoring.

The book delves deeply into the emerging threats posed by Large Language Models (LLMs) and Generative AI, specifically addressing prompt injection, tool abuse, and data leakage through memorization. It argues that the natural language interface of modern AI creates a vast, unpredictable attack surface that traditional cybersecurity measures are ill-equipped to handle. To combat these risks, the author advocates for "secure-by-design" MLOps, where automated adversarial testing, red teaming, and strict governance are integrated directly into the continuous integration and deployment pipelines.

Beyond technical controls, the text highlights the critical intersection of AI with governance, risk, and compliance (GRC). It provides actionable roadmaps for navigating the evolving global regulatory landscape, such as the EU AI Act and NIST frameworks, while emphasizing the ethical necessity of bias mitigation and transparency. The book concludes by stressing that as AI moves toward greater autonomy, organizations must adopt a proactive, adaptive stance—combining hardware-backed security like Trusted Execution Environments with rigorous incident response playbooks to maintain trust in an increasingly hostile digital environment.

What You'll Find Inside:

• Understand the full ML lifecycle attack surface, from data collection and training to deployment, monitoring, and supply chain, and how to threat‑model each stage.
• Learn core adversarial techniques—data poisoning, backdoors, evasion attacks, model extraction, and privacy leaks—and their practical impacts on model integrity and confidentiality.
• Explore defensive strategies including robust training, certified robustness, differential privacy, secure inference (TEEs, MPC, HE), and watermarking to protect data and models.
• Discover LLM‑ and GenAI‑specific threats such as prompt injection, tool abuse, and memorization, along with alignment, filtering, and guardrail techniques to mitigate them.
• Gain practical methodologies for red teaming, continuous monitoring, incident response, and governance to build secure‑by‑design ML platforms and manage risk throughout the AI lifecycle.

Who's It For:

This book is written for security engineers and machine learning teams responsible for reducing risk in AI systems, but it is equally valuable for product managers, architects, and leaders who must balance model capability, cost, and assurance. Anyone involved in designing, deploying, or governing ML workloads—especially those handling sensitive data or operating in regulated environments—will find actionable patterns, playbooks, and decision frameworks to strengthen resilience against adversarial attacks and misuse.