Governance checklist
Enterprise AI governance checklist for adoption visibility
AI governance fails when it lives only in policy documents. It becomes operational when permissions, identity, audit, evidence, ownership, and measurement are visible in the same places teams ask questions and run workflows.
Key takeaways
- Governance should cover context selection, not only final outputs.
- Identity, permissions, classifications, and audit have to travel with AI-enabled work.
- Good governance helps teams scale useful AI adoption instead of simply slowing them down.
Controls to confirm early
Before scaling AI across teams, confirm how the platform handles source permissions, team boundaries, private work, restricted context, classification ceilings, role mapping, and access reviews.
- Source permissions
- SSO and SCIM direction
- Classification ceilings
- Access reviews
Evidence and audit questions
Ask whether answers, recommendations, agent actions, and metrics can be traced back to source evidence. The audit trail should explain what context was used, which policy decisions applied, and what action followed.
- Graph access
- Agent activity
- Policy decisions
- Connector events
Ownership and improvement
Governance is stronger when it has owners and feedback loops. Each recommendation should show who can act, what outcome is expected, and how the organisation will measure whether the change helped.