"Audit-Ready GenAI: Logging, Evidence, and Explainability Without Killing Velocity"
GenAI teams are shipping fast—and then discovering that "we can explain it" isn't the same as "we can prove it." This book is for experienced engineers, platform teams, security practitioners, and technical leaders who need their GenAI systems to survive audits, customer scrutiny, and incident investigations without turning delivery into paperwork.
You'll learn how to treat audit readiness as an engineering capability: build evidence-by-default architectures; map risks to concrete controls; scope and inventory the real AI system (models, prompts, tools, RAG sources, and environments); and instrument evidence-grade telemetry with durable correlation IDs. The book shows how to construct end-to-end lineage from user request to output, govern RAG pipelines with provenance and retrieval evidence, and deliver explainability that is defensible because it is linked to verifiable artifacts rather than model-generated rationales. It also covers log integrity, retention, privacy minimization, and separation of duties so the evidence itself can be trusted.
Expect deep, implementation-oriented guidance with operational flows for releases, monitoring, and incident RCA. Familiarity with distributed systems, CI/CD, and modern observability is assumed; GenAI platform experience helps, but the book builds the audit-ready mental model from first principles.
