AI Labs Gen Limited

AI Governance Framework

Published by AI Labs Gen Limited

An AI governance framework turns an AI use case into an operating system: it names the owner, defines the decision boundary, and records the evidence needed to review what happened.

A three-step method

  1. 01

    Map the use case and its boundary

    Identify the business decision, the inputs the system can use, the output it may produce, and the actions it must never take.

  2. 02

    Assign owners and approval points

    Name the business owner, technical owner, and approver for sensitive outcomes so escalation is a defined process rather than an improvised response.

  3. 03

    Run with evidence and a review cadence

    Record material inputs, actions, approvals, exceptions, and changes. Review the evidence on a cadence that matches the use case risk.

Operational test before scale

Start with one bounded workflow rather than a company-wide policy exercise. Document the business outcome, the data the workflow may use, the people affected, and the point at which a human must intervene. This creates a control surface that a business owner can understand and a technical team can implement.

Before scale, run representative normal, failure, and edge cases. The review should confirm that owners can pause the workflow, explain a material decision, recover from an error, and find the evidence needed for an incident review. Governance is effective only when these controls work in the operating system, not only in a policy document.

Essential controls

  • A written purpose and data boundary for each workflow.
  • Named owners for decisions, changes, and incidents.
  • Human review before high-impact or policy-sensitive actions.
  • An audit trail that makes exceptions and changes visible.

FAQ

Is an AI governance framework only a policy document?

No. It connects policy to operating controls such as approval steps, access rules, logs, and an escalation runbook.

When should a team define AI governance?

Before a workflow reaches production, especially when it uses business data, affects customers, or can trigger actions outside the team.