What AI governance actually requires
Monitoring, evaluation, and audit logging — specified as concrete engineering requirements, not policy prose that no one can implement.
AI governance fails when it is written as policy prose and handed to a team that has no way to implement it. It works when it is specified as engineering requirements the system must satisfy before it ships.
Enterprise buyers read the governance chapter twice. They have seen what happens when an ungoverned model reaches production: a decision no one can explain, a data exposure no one logged, a regulator asking for evidence that does not exist. Governance is how you make sure none of that is your story.
Three requirements, written for engineers
Monitoring
The system must emit drift, cost, latency, and behavior signals continuously, with alerting before users notice a problem. "We will monitor the model" is not a requirement. "P95 latency, token cost per request, refusal rate, and output-distribution drift are tracked per workflow with alert thresholds" is.
Evaluation
There must be a task-specific evaluation suite that runs before every release and on a schedule in production. A model change that degrades quality should fail the build, not surface as a customer complaint three weeks later.
Audit logging
Every consequential decision must be written to an immutable log with inputs, outputs, model version, and the reasoning trace. This is what turns "trust us" into "here is the evidence."
Governance is built into the system, not documented after the fact.
Map controls as the system runs
SOC 2 and ISO 27001 do not have to be a scramble before an audit. When the system is designed to emit evidence — access logs, change records, evaluation results — compliance becomes a query against data you are already collecting. Map each control to the signal that satisfies it, and the audit becomes a report rather than an excavation.
Treat regressions as incidents
The most common governance failure after launch is the silent regression: someone swaps a model or edits a prompt, quality drops, and no one notices until it matters. The discipline that prevents this is simple to state and hard to skip — every model or prompt change re-runs the evaluation harness before release, and a failure blocks the release.
Key takeaways
- Governance is an engineering requirement, not a document written after the system is built.
- Specify monitoring, evaluation, and logging as acceptance criteria the system must meet to ship.
- Map controls to SOC 2 and ISO 27001 as the system runs — collect evidence continuously.
- Re-run evaluations on every model or prompt change; treat silent regressions as incidents.