01
Define data and access boundaries
Each workflow should define the data it needs, what must stay outside scope and which systems the implementation may access.
Minimum access is a stronger starting point than giving broad permissions because a workflow might need them later.
02
Make human accountability explicit
Teams need to know what the AI can do, what requires approval and when an exception must move to a person.
The level of review should reflect the consequence of the decision rather than treating every AI use case the same way.
03
Govern the operating lifecycle
Controls should cover testing before launch, monitoring in production, ownership, incident response and how material changes are reviewed.
Governance is most useful when it travels with the workflow rather than sitting in a policy nobody uses.