Responsible AI resource
AI governance checklist
AI governance becomes operational when every system has a defined purpose, owner, risk level, approved data, evaluation criteria, human oversight, monitoring and change process.
Direct answer
What you should know
This checklist helps product and engineering teams turn broad responsible-AI commitments into specific evidence and controls before and after launch.
Purpose and accountability
- Document the intended use, users and prohibited uses.
- Name business, product, technical and risk owners.
- Define approval and exception authority.
- Record affected users and potential consequences.
Data and system controls
- Confirm source, permission, sensitivity and retention.
- Limit model, tool and user access according to need.
- Test input, retrieval and output boundaries.
- Protect secrets and log consequential actions.
Quality and operation
- Create representative evaluation cases.
- Define human review and escalation.
- Monitor quality, drift, incidents, latency and cost.
- Re-evaluate material model, prompt, data or workflow changes.
Step-by-step process
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Register the system
Record owner, purpose, users, model, data, tools, integrations and environment.
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Classify risk
Assess impact, sensitivity, autonomy, reversibility, affected users and applicable obligations.
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Define requirements
Specify approved use, prohibited use, data controls, quality thresholds, oversight and logging.
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Evaluate before release
Test representative, edge, adversarial and permission-boundary cases against acceptance criteria.
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Approve and document
Record the decision, evidence, limitations, responsible owners and operational conditions.
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Monitor and re-evaluate
Track incidents, feedback, performance and material changes throughout operation.