An AI Governance Checklist for Enterprises

AI has moved from experiment to enterprise infrastructure — and regulation has caught up. Between the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001 and sector-specific rules, "we'll govern it later" is no longer a viable strategy. This checklist covers the controls every regulated organization should have in place before AI touches a meaningful decision.

Use it as a gap analysis: if you can confidently tick most boxes, you're in good shape. Where you can't, that's where risk — and audit findings — hide.

1. Strategy & ownership

• Every AI use case has a clear business purpose and a defined measure of success.
• A named individual or committee owns AI governance across the organization.
• "No-go" zones are defined — uses where the risk is too high to permit.

2. Risk classification

• Each AI use is classified by risk (impact on people, decisions, data and compliance).
• Classification maps to external frameworks where relevant (e.g. EU AI Act risk tiers).
• Higher-risk uses trigger stronger controls and sign-off.

3. Data governance

• Data sources are documented, lawful and fit for purpose, with clear lineage.
• Personal and sensitive data is handled per applicable privacy law (GDPR, HIPAA, etc.).
• Confidential or regulated data is never sent to ungoverned public AI tools.

4. Model & vendor due diligence

• AI vendors are assessed for security, compliance posture and data-use terms.
• Model purpose, limitations and intended use are documented and validated for that use.
• Model versions and changes are controlled and traceable.

5. Human oversight & accountability

• A qualified human reviews and approves AI output before it drives a regulated decision.
• The level of oversight is proportionate to the risk of the use case.
• Clear escalation paths exist when output is wrong, uncertain or out of scope.

6. Documentation & audit-readiness

• AI use is documented: purpose, data, model, controls, validation and owners.
• Decisions influenced by AI are traceable and reconstructable.
• You could explain to an auditor how each system works and how it's controlled.

7. Monitoring & lifecycle

• Outputs are monitored for drift, error and degradation over time.
• A defined process handles updates, retraining and retirement.
• A rollback or contingency plan exists if a system must be switched off.

8. Policy & training

• A written AI policy defines acceptable use, roles and approval gates.
• Staff are trained on safe, compliant AI use.
• Governance keeps pace with evolving regulation.

Most organizations have some of these in place and gaps in others. The fastest way to find your gaps is a structured assessment that maps your current state against the frameworks you're held to — and produces a prioritized plan to close them.