Regulatory attention to AI is increasing. When regulators examine your AI systems, preparation determines outcomes. This guide provides a systematic approach to AI compliance audit preparation.
Executive Summary
- Regulatory scrutiny is growing — Financial regulators, data protection authorities, and sector regulators examining AI
- Preparation is essential — Organizations that prepare fare better than those caught off-guard
- Compliance documentation matters — Regulators want evidence of compliance, not just assertions
- Scope understanding critical — Know what regulations apply and how they apply to your AI
- Gaps better identified internally — Find and fix issues before regulators do
- Response capability counts — Ability to respond promptly to regulator requests signals governance maturity
AI Compliance Audit Preparation SOP
Phase 1: Regulatory Mapping (Weeks 1-2)
Identify applicable requirements:
- Data protection (PDPA Singapore, PDPA Malaysia)
- Sector-specific (MAS for finance, MOE for education)
- AI-specific guidance (IMDA AI Governance Framework)
- Consumer protection
- Employment law (for HR AI)
Document for each regulation:
- What requirements apply to AI?
- Which AI systems are in scope?
- What compliance evidence is needed?
- What are the penalties for non-compliance?
Phase 2: Gap Assessment (Weeks 3-4)
Assess compliance status:
- Review current AI systems against requirements
- Identify documentation gaps
- Assess control effectiveness
- Prioritize gaps by risk
Common compliance gaps:
- Missing DPIAs for AI systems
- Inadequate consent for AI processing
- No AI system inventory
- Missing bias testing documentation
- Inadequate transparency/disclosure
Phase 3: Remediation (Weeks 5-8)
Address identified gaps:
- Prioritize high-risk gaps
- Create remediation plans
- Implement fixes
- Document remediation
Phase 4: Documentation Preparation (Weeks 9-10)
Organize compliance evidence:
- Policy documents
- Risk assessments
- Testing results
- Training records
- Incident logs
- Governance records
Phase 5: Response Capability (Weeks 11-12)
Prepare for regulator engagement:
- Identify key contacts
- Brief interviewees
- Prepare response templates
- Establish coordination process
Compliance Documentation Checklist
Governance:
- AI governance policy
- Committee charter and minutes
- Roles and responsibilities
- Approval records
Risk and Controls:
- AI risk assessments
- Control documentation
- Testing evidence
- Audit findings and remediation
Data Protection:
- DPIAs for AI systems
- Consent records
- Data processing agreements
- Cross-border transfer documentation
Transparency:
- AI disclosure notices
- Explanation documentation
- Customer communication records
Fairness:
- Bias testing results
- Fairness criteria documentation
- Remediation evidence
Regulatory Examination Tips
Before:
- Review previous examination findings
- Update documentation
- Brief key personnel
- Test response capability
During:
- Coordinate responses centrally
- Respond promptly and completely
- Be honest about gaps
- Document all interactions
After:
- Address findings promptly
- Track remediation
- Update processes to prevent recurrence
Disclaimer
This guide provides general preparation guidance. Regulatory requirements vary by jurisdiction and sector. Engage qualified legal and compliance counsel for specific regulatory obligations.
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References
- Singapore PDPC. (2024). "Guide to AI and PDPA."
- MAS. (2024). "Supervisory Expectations on AI."
- IMDA. (2024). "AI Governance Framework Implementation."
Frequently Asked Questions
Organize documentation, ensure audit trails are complete, review policy compliance, prepare to demonstrate human oversight, and brief relevant staff on audit procedures.
Maintain AI inventory, governance policies, risk assessments, approval records, bias testing results, human oversight logs, vendor due diligence, and incident records.
Regulators examine governance structures, risk management, human oversight, documentation, incident response, and whether actual practices match stated policies.
References
- Guide to AI and PDPA.. Singapore PDPC (2024)
- Supervisory Expectations on AI.. MAS (2024)
- AI Governance Framework Implementation.. IMDA (2024)
