"AI Incident Response: Playbooks for Prompt Leaks, Tool Abuse, and Model Failures"
LLM-powered products fail differently: a single prompt injection can turn retrieval into an exfiltration channel, a tool call into a real-world breach, or a "small" model change into a silent safety regression. This book is written for experienced engineers, security teams, SREs, and AI platform owners who need to treat these behaviors as first-class incidents—triaged, contained, investigated, and prevented with the same rigor as traditional security and reliability events.
You'll learn a complete incident-response discipline tailored to AI systems: operational definitions and severity for probabilistic failures; an incident-driven reference architecture spanning prompting, RAG, tools, and memory; and end-to-end telemetry and detection engineering built around traceable provenance and versioning. The core of the book is three deep playbooks—prompt leaks, tool abuse, and model failures—each covering indicators, containment controls (kill switches, permission downgrades, isolation), forensics to reconstruct context and side effects, eradication through deterministic authorization and hardening, and recovery gated by regression evals, canaries, and automated rollback.
Expect a practical, operations-first treatment: decision criteria, failure modes, and workflows you can adapt to on-call reality. Readers should be comfortable with modern cloud architectures, API-based model providers, and the fundamentals of security and incident response; the differentiator here is turning "LLM weirdness" int
