Adversarial AI Attacks, Mitigations, and Defense Strategies

As AI systems take on more consequential roles across industries, the question of how those systems can be attacked, subverted, or deceived has moved from academic curiosity to urgent operational concern. Adversarial AI Attacks, Mitigations, and Defense Strategies addresses that concern with serious technical depth. John Sotiropoulos covers the full landscape of adversarial threats to AI systems — from data poisoning and model inversion attacks to prompt injection and adversarial examples designed to fool classifiers — and pairs each attack type with concrete threat modeling approaches and corresponding defense strategies. The book introduces MLSecOps as a discipline for operationalizing AI security practices, drawing a parallel with how DevSecOps integrated security into software development pipelines, and argues for embedding security into the AI development lifecycle rather than treating it as a post-deployment concern. At 586 pages, this is one of the more comprehensive treatments of adversarial AI security currently available, covering enough technical ground to serve as a genuine reference for security engineers and AI practitioners working at this intersection. For organizations deploying AI in contexts where security matters — which increasingly means most of them — understanding these attack surfaces and defense strategies is no longer optional reading.

Author:
John Sotiropoulos