AI Compliance Checklist: Preparing for Regulatory Requirements

Regulatory requirements for AI are emerging across jurisdictions. Organizations that prepare now—building governance, documentation, and controls—will comply more easily and at lower cost. This checklist provides a practical framework for AI compliance readiness.

Executive Summary

• Compliance preparation is proactive investment. Building governance before enforcement is cheaper than scrambling after.
• Risk-based prioritization is essential. Focus effort on high-risk AI applications first.
• Documentation is foundational. Regulators expect evidence of governance, not just assertions.
• Human oversight is universally required. All frameworks emphasize human accountability for AI decisions.
• Testing and validation must be ongoing. Point-in-time assessments are insufficient.
• Transparency requirements are increasing. Disclosure to users and affected parties is becoming standard.
• Cross-functional effort is needed. Legal, IT, business, and risk all play roles.
• Continuous monitoring enables adaptation. Regulations evolve; compliance programs must too.

Why This Matters Now

The compliance landscape for AI is transitioning:

• From guidelines to requirements: Voluntary frameworks are becoming mandatory.
• From awareness to enforcement: Regulators are moving from education to action.
• From general to specific: Broad principles are being operationalized into specific obligations.
• From domestic to international: Cross-border requirements are increasing.

Master AI Compliance Checklist

Category 1: Governance and Accountability

AI Governance Structure:

• AI governance policy documented and approved
• Accountability roles clearly assigned (who is responsible for AI)
• Governance committee or oversight body established
• Escalation procedures for AI issues defined
• Board oversight mechanism for AI risk

Policy Framework:

• AI acceptable use policy in place
• AI ethics principles documented
• AI procurement requirements defined
• AI incident response procedures established
• Regular policy review schedule set

Category 2: AI System Inventory and Classification

Inventory:

• All AI systems identified and cataloged
• Purpose of each system documented
• Data processed by each system mapped
• Actions/decisions enabled by each system recorded
• Ownership assigned for each system

Risk Classification:

• Risk assessment methodology defined
• Each AI system classified by risk level
• High-risk systems identified and flagged
• Prohibited use cases identified and blocked
• Classification reviewed regularly

Category 3: Documentation and Records

Technical Documentation:

• System design and architecture documented
• Training data sources and characteristics recorded
• Model performance metrics documented
• Testing and validation results maintained
• Known limitations documented

Operational Documentation:

• User instructions and guidelines created
• Administrator procedures documented
• Incident response procedures for AI created
• Change management processes for AI defined
• Audit trails implemented

Compliance Documentation:

• Regulatory mapping completed (which rules apply)
• Compliance assessments documented
• Gap remediation tracked
• Evidence repository maintained
• Audit-ready package prepared

Category 4: Risk Management

Risk Assessment:

• AI-specific risk assessment conducted
• Risks identified and documented
• Risk mitigation measures defined
• Residual risk accepted or escalated
• Risk assessment regularly updated

Risk Controls:

• Controls mapped to identified risks
• Control effectiveness tested
• Control gaps identified and remediated
• Control monitoring in place
• Control documentation maintained

Category 5: Data Governance

Data Protection:

• Lawful basis for AI processing established
• Data minimization applied to AI
• Data accuracy requirements addressed
• Data retention policies for AI defined
• Data security controls implemented

Consent and Rights:

• Consent obtained where required
• Data subject rights processes include AI
• Opt-out mechanisms available where required
• Access and correction processes work for AI decisions
• Deletion processes include AI-related data

Data Protection Impact:

• DPIA triggers for AI identified
• DPIAs conducted for high-risk AI
• DPIA recommendations implemented
• DPIA reviewed and updated regularly

Category 6: Transparency and Disclosure

User Transparency:

• Users informed when interacting with AI
• AI decision factors explained where required
• Limitations of AI disclosed
• Human alternative available where required
• Feedback mechanisms provided

Organizational Transparency:

• AI use disclosed in privacy notices
• Stakeholder communications address AI
• Regulatory disclosures include AI
• Annual reports address AI governance

Category 7: Human Oversight

Oversight Mechanisms:

• Human oversight defined for each AI system
• Oversight roles and responsibilities assigned
• Intervention capabilities verified
• Override mechanisms tested
• Oversight effectiveness monitored

Decision Points:

• When AI decisions require human review identified
• Review processes implemented
• Reviewers trained and capable
• Review decisions documented
• Review quality monitored

Category 8: Testing and Validation

Pre-Deployment Testing:

• Functional testing completed
• Performance testing completed
• Security testing completed
• Bias testing completed
• Edge case testing completed

Ongoing Validation:

• Performance monitoring in place
• Drift detection implemented
• Periodic re-testing scheduled
• User feedback incorporated
• Model updates validated before deployment

Category 9: Fairness and Non-Discrimination

Bias Assessment:

• Potential bias sources identified
• Training data reviewed for bias
• Output testing for discriminatory patterns conducted
• Demographic impact analyzed where applicable
• Bias mitigation measures implemented

Fairness Monitoring:

• Fairness metrics defined
• Ongoing monitoring implemented
• Disparate impact reviewed
• Remediation process defined
• Regular fairness reporting

Category 10: Security

AI-Specific Security:

• Prompt injection protections implemented
• Data leakage controls in place
• Model security controls implemented
• API security verified
• AI incident detection capabilities

General Security:

• Access controls implemented
• Encryption at rest and in transit
• Audit logging enabled
• Vulnerability management includes AI
• Incident response includes AI scenarios

Category 11: Vendor Management

Vendor Assessment:

• AI vendor security assessed
• Vendor data practices reviewed
• Certifications verified
• Contractual protections in place
• Ongoing monitoring established

Contracts:

• Data processing agreements executed
• Compliance requirements flowed down
• Audit rights secured
• Incident notification terms agreed
• Liability terms appropriate

Category 12: Training and Awareness

Staff Training:

• AI governance training developed
• Role-specific training created
• Training completion tracked
• Regular refresher training scheduled
• Training effectiveness measured

Awareness:

• Ongoing awareness program active
• Policy updates communicated
• Regulatory changes shared
• Best practices disseminated

Category 13: Incident and Breach Response

Incident Response:

• AI incident classification defined
• Response procedures documented
• Response team identified and trained
• Communication templates prepared
• Post-incident review process defined

Breach Notification:

• Notification triggers defined
• Notification timelines documented
• Notification templates prepared
• Regulatory contact information current
• Notification drill conducted

Category 14: Continuous Improvement

Review and Update:

• Regular compliance review scheduled
• Gap tracking maintained
• Remediation prioritized and tracked
• Lessons learned incorporated
• Framework updates made

Regulatory Monitoring:

• Regulatory change monitoring in place
• Impact assessment process defined
• Implementation tracking for new requirements
• Legal/regulatory counsel engaged

Implementation Priority

If you can't do everything at once:

Phase 1 (Immediate):

• AI system inventory
• Risk classification
• High-risk AI documentation
• Governance accountability

Phase 2 (30 days):

• Data protection compliance
• Human oversight mechanisms
• Security controls
• Vendor assessment

Phase 3 (90 days):

• Full documentation
• Testing and validation
• Training programs
• Ongoing monitoring

Metrics to Track

FAQ

Q: Which items are most critical? A: Inventory, risk classification, and accountability. You can't comply with requirements you don't know apply.

Q: How long does full compliance take? A: For a mature organization, 3-6 months for foundational compliance. Ongoing maintenance is continuous.

Q: Do we need everything for every AI system? A: No. Apply proportionately based on risk. High-risk AI needs everything; minimal-risk AI needs basic governance.

Q: What if we find gaps we can't immediately fix? A: Document the gap, implement compensating controls where possible, and prioritize remediation.

Next Steps

Use this checklist alongside jurisdiction-specific guidance:

• AI Regulations in 2026: What Businesses Need to Know
• AI Regulations in Singapore: IMDA Guidelines and Compliance Requirements
• Data Protection Impact Assessment for AI: When and How to Conduct One

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Disclaimer

This checklist provides general guidance on AI compliance preparation. Requirements vary by jurisdiction and industry. Organizations should consult qualified legal counsel for specific compliance obligations.

References

1. European Union. EU AI Act Implementation Guidelines.
2. Singapore IMDA. Model AI Governance Framework Implementation Guide.
3. ISO/IEC 42001:2023. AI Management System Requirements.
4. NIST. AI Risk Management Framework.
5. OECD. AI Policy Implementation Tools.