Production AI governance works when it is embedded in the workflow: who can access data, what the system may do, when humans review and how incidents are handled.
Govern the workflow, not only the model
Policies are necessary, but production risk appears in actual handoffs. A model may summarize, classify or recommend, but the workflow decides who sees the output and what happens next.
Governance should describe the allowed action, the review owner and the escalation path for each high-impact use case.
Every AI action should have an owner, a boundary and a review path.
Make decisions inspectable
Teams need enough context to inspect outputs: source references, prompt version, data inputs, confidence signals and reviewer feedback.
This evidence helps users trust the system and helps risk teams investigate problems without reconstructing them manually.
If a decision affects a customer, claim, record or operation, retain enough context to explain it.
Governance is an operating routine
AI systems change as data, models, prompts and user behavior change. Governance has to include monitoring, review cadence, access checks and retirement rules.
Treating governance as an ongoing routine keeps teams from relying on documents that no longer reflect the system.
Review exceptions and user corrections regularly. They are the fastest signal that policy and reality have drifted.