Preparing for an AI Audit: A Comprehensive Readiness Guide

Whether it's internal audit, external auditors, or a regulatory examination, AI audits are becoming common. Organizations that prepare systematically demonstrate governance maturity and avoid uncomfortable surprises.

Executive Summary

• Audits are coming — Internal audit, external auditors, and regulators are examining AI governance
• Preparation beats reaction — Organizations that prepare systematically fare better
• Four preparation pillars — Documentation, controls, evidence, and people readiness
• Common gaps are predictable — Most organizations struggle with the same issues
• Start 90 days before — Meaningful preparation requires time
• Evidence matters most — Auditors want proof, not promises
• Post-audit is ongoing — Address findings promptly; demonstrate continuous improvement

Why This Matters Now

Regulatory Attention. MAS, PDPC, and sector regulators have AI on their radar. (/insights/ai-regulations-singapore-imda-compliance) (/insights/pdpa-ai-compliance-singapore-guide)

Internal Audit Mandate. Internal audit functions are adding AI to their audit plans.

Board Expectation. Directors want assurance that AI is governed appropriately. (/insights/ai-board-risk-oversight-structure)

What Auditors Examine

Governance Framework: Structure, accountability, policies, roles Risk Management: Identification, assessment, mitigation, monitoring (/insights/ai-risk-assessment-framework-templates) Model Lifecycle: Development, testing, deployment, retirement Compliance: Regulatory mapping, data protection, industry requirements (/insights/ai-compliance-checklist-regulatory-preparation) Controls: Technical, operational, management, third-party

AI Audit Preparation SOP

Phase 1: Scoping (Days 1-15)

1. Confirm audit scope — Systems, aspects, time period, documents
2. Identify stakeholders — Audit lead, interviewees, coordinators
3. Review previous audits — Findings, remediation, recurring themes

Phase 2: Documentation Preparation (Days 16-45)

1. Complete AI inventory — All systems documented, owners identified
2. Review policies — Governance, acceptable use, approval process (/insights/ai-policy-essential-components)
3. Risk documentation — Register, assessments, mitigations (/insights/ai-risk-register-template)
4. Compliance documentation — Requirements, status, evidence
5. Meeting records — Minutes, decisions, action items

Phase 3: Control Validation (Days 46-60)

1. Test technical controls — Access, logging, security, data protection
2. Test operational controls — Procedures, training, incidents
3. Test management controls — Reporting, reviews, approvals
4. Test vendor controls — Assessments, contracts, monitoring (/insights/ai-vendor-security-assessment-checklist)

Phase 4: Gap Remediation (Days 61-80)

1. Prioritize gaps — By severity and effort
2. Remediate where possible — High-priority first
3. Document remaining gaps — With plans and timelines

Phase 5: Evidence Preparation (Days 81-90)

1. Organize evidence — Create index, organize by topic
2. Prepare interviewees — Brief on scope, review questions
3. Establish logistics — Schedules, workspaces, technology
4. Prepare opening briefing — Overview, governance, status, issues

Documentation Checklist

Governance:

• AI governance policy
• AI strategy document
• Committee charter and minutes
• Board AI updates (/insights/ai-board-reporting-template-updates)
• Roles and responsibilities

Risk:

• AI risk framework
• AI risk register
• Individual risk assessments
• Incident log and response plan (/insights/ai-incident-response-plan)

Compliance:

• Regulatory requirements mapping
• Compliance status report
• Data protection impact assessments (/insights/data-protection-impact-assessment-ai-dpia)

Operations:

• AI system inventory (/insights/ai-model-inventory-document-track-systems)
• Approval and testing records
• Monitoring reports
• Training records

Vendors:

• Vendor risk assessments
• Contracts with AI terms
• Exit plans

Common Audit Findings

1. Incomplete AI Inventory — Shadow AI is prevalent
2. Outdated Policies — Not updated for AI
3. Missing Risk Assessments — Deployed without assessment
4. Inadequate Vendor Due Diligence — Third-party AI not assessed
5. Insufficient Documentation — Decisions without rationale
6. Weak Monitoring — No ongoing performance tracking
7. Training Gaps — Staff untrained on AI tools
8. Incident Response Gaps — No AI-specific procedures

During and After the Audit

During:

• Coordinate responses through single point of contact
• Be responsive and honest
• Document everything
• Escalate appropriately

After:

• Review draft findings for accuracy
• Accept valid findings
• Develop remediation plan with owners and dates
• Track and report remediation progress

Checklist: AI Audit Readiness

• Audit scope confirmed
• Previous findings reviewed
• Documentation complete
• Controls tested
• Gaps identified and prioritized
• Remediation completed where possible
• Evidence organized
• Interviewees prepared
• Logistics confirmed

Frequently Asked Questions

Ready to Prepare for AI Audit?

Audit readiness demonstrates governance maturity.

Book an AI Readiness Audit to identify gaps before internal or external auditors do.

[Contact Pertama Partners →]

References

1. IIA. (2024). "Auditing Artificial Intelligence."
2. ISACA. (2024). "AI Audit Program."
3. Deloitte. (2024). "AI Audit Readiness Assessment."
4. MAS. (2023). "Technology Risk Management Guidelines."