AI Safety and Alignment Engineering - Guardrails, red-teaming, evaluation, and responsible deployment for production AI systems. #1007458

AI Safety and Alignment Engineering is a practical engineering guide to building AI systems that are safe, aligned, and trustworthy in production. It starts from a single observation: safety in AI is not a philosophical problem or a governance problem at the point of production — it is an engineering discipline with measurable requirements, testable controls, and failure modes that must be understood before they occur.

The book walks through the full stack — why AI safety is an engineering problem and not an aspiration, the failure modes that AI systems exhibit from capability to alignment to specification to emergence, alignment fundamentals from instruction tuning to RLHF to DPO, guardrails and constrained generation, red-teaming and adversarial testing against the MITRE ATLAS taxonomy, safety evaluation and the calibration of automated judges, prompt injection and jailbreak defence with the honest truth about what works, interpretability and mechanistic analysis, governance and regulatory compliance across the EU AI Act and NIST AI RMF, responsible deployment practices with staged rollouts and shadow mode, monitoring and incident response for AI systems, and the trends reshaping the field.

It covers the failure modes that quietly wreck AI deployments: a jailbreak that bypasses safety training, a reward model that rewards verbose hedging over honest refusal, a guardrail that blocks legitimate requests while missing the actual attack, a prompt injection hidden in a retrieved document, a safety evaluation that passes on the benchmark and fails on the tail, a model upgrade that silently changes refusal behaviour, a compliance gap that only appears when a regulator asks the right question. Each is presented with the failure, the countermeasure, and the operational tradeoff.