AI GOVERNANCE
An AI governance operating model makes useful action safe to scale.
A practical AI governance framework for leaders defining agent purpose, access, action boundaries, human escalation, evaluation, and evidence.
What belongs in an enterprise AI governance framework?
Governance should make it clear what an AI system is for, what it can access, what it may recommend or change, who owns exceptions, and how the organisation will know whether it remains useful. It is an operating model for decisions, not a policy document added after a pilot ships.
CIO, CDO, risk, compliance, security, operations
Define the control boundary before an agent is allowed to act in a live workflow.
A shared control map connecting purpose, access, human accountability, and evidence.
Give every agent a declared purpose
An approved agent should have a named workflow, affected users, expected outcome, and accountable owner. This prevents a general-purpose capability from silently becoming a shadow operating system.
- Workflow and user group in scope.
- Business outcome and measure.
- Accountable owner and review cadence.
Separate context from action rights
Access to customer or operational context does not automatically confer a right to act. The design must make that difference visible to the people who operate and govern the workflow.
- What context may be retrieved and shown.
- What the system may draft, recommend, or execute.
- When human approval, escalation, or a hard stop is required.
Treat evaluation as an operating control
A governance model needs evidence. Review quality, exception patterns, user reliance, and business outcomes after release so a team can tune, pause, or retire a workflow responsibly.
- Traceable decision and escalation history.
- Defined quality and safety checks.
- A process for correcting drift or removing a weak workflow.
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