The policy can be correct while the workflow is still unsafe.
Most AI governance begins with model selection, acceptable-use policy, privacy and security controls. Those are necessary, but the operational risk usually appears later, when an AI recommendation becomes a task, a customer message, a system update or an approval request.
At that point the important question is no longer only what the model can do. It is who is allowed to accept the recommendation, what evidence supports it, which system is authoritative and whether the action requires additional approval.
Capability is not authority.
A model may be technically capable of drafting, deciding or triggering an action. That capability should not automatically become permission to change a production system.
Workflow-level governance separates reasoning from execution. The model can explain, prepare or recommend while roles, workflow state, provider readiness and approval rules determine what is actually allowed to happen.
- Reasoning should be traceable to permitted operating context.
- Recommendations should remain distinguishable from approved actions.
- Sensitive execution should retain explicit authority checks.
- Human review should be placed where accountability requires it, not everywhere by default.
Govern the transition points.
The most useful governance design focuses on transition points: AI to human, human to system, system to system and one team to another. Those are the moments where context is lost and authority becomes ambiguous.
A governed orchestration layer can make those transitions explicit without forcing every underlying application to be replaced.