Your AI feature works. Now prove it's governed.

The single choke point that makes governance enforceable rather than optional — provider abstraction, error taxonomy, failover, and the hook pipeline every later module plugs into.

When a human gate is real oversight and when it is theatre — selective routing, escalation, queue starvation, and the review screen that decides whether either happens.

Why application logs are not audit logs, a full request-lifecycle schema, append-only storage, and designing so that erasure is a query rather than a crisis.

Attribution before enforcement, token-aware limiting with reserve-and-settle, effective-dated pricing, and the per-trace budget that stops a runaway agent loop.

Why filtering is detection and not a boundary, the architectural controls that actually hold, and building a DLP scanner that logs categories rather than values.

The four things people call drift, pinning and deprecation planning, a golden-set evaluation harness, and the production signals you are already discarding.

What auditors and enterprise customers actually ask, the mapping layer from claim to query, deterministic reports, and subject access as an engineering workflow.

Where personal data really enters an AI system, minimisation as a whitelist rather than a filter, crypto-shredding for erasure, and the DPIA and subprocessor triggers to recognise.

Sequencing in five phases where each is independently useful, avoiding quiet bypass of the gateway, and making the case to the audience whose support you need.