Description

What if your AI system never fails—yet is already compromised?

Most AI security failures don't arrive as breaches, alerts, or outages. They arrive quietly. Models keep producing outputs. Pipelines keep running. Metrics remain within tolerance—while trust, integrity, and control erode beneath the surface.

AI Supply Chain Security confronts this uncomfortable reality head-on. Rather than treating the trained model as the locus of risk, this book reframes security as a property of the entire machine-learning supply chain: data sourcing, preprocessing, training logic, dependency graphs, infrastructure, deployment, and feedback loops. It argues that the most dangerous vulnerabilities emerge not from spectacular attacks, but from structural conditions that reward silence, scale, and statistical continuity.

Grounded in adversarial ML research, systems security, and socio-technical analysis, this book challenges the persistent myth of the "secure model" and replaces it with a pipeline-centric understanding of risk—one better suited to modern, adaptive AI systems.

Inside, you'll encounter:

Why poisoned data and backdoored representations rarely trigger alarms How distributional drift degrades trust unevenly across populations The limits of traditional MLOps controls in adversarial environments Why reproducibility can coexist with systemic fragility How incentives, governance gaps, and platform economics shape security outcomes A framework for analyzing AI risk as cumulative rather than event-driven

This is not a checklist or a vendor playbook. It is a conceptual and operational recalibration for practitioners, researchers, security teams, and technical leaders who suspect that current AI security conversations are asking the wrong questions.

If you build, deploy, regulate, or depend on machine-learning systems, this book gives you the language—and the lens—to see what usually goes unnoticed.