aims-audit

AIMS Audit Prep (ISO 42001)

Operational playbook for ISO 42001:2023 AI Management System (AIMS) audit preparation. Whether targeting initial certification, surveillance audit, or annual internal audit.

When to use this skill vs. iso42001-ai-management:

• This skill: audit imminent; need readiness sprint
• iso42001-ai-management: building AIMS from scratch; multi-quarter program

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When to use this skill

| Situation | Skill applies |

|-----------|---------------|

| ISO 42001 Stage 1 audit scheduled | Yes — documentation review prep |

| Stage 2 (onsite/operational) audit | Yes — operational evidence sprint |

| Annual surveillance audit | Yes — surveillance prep |

| Internal AIMS audit | Yes — internal audit playbook |

| AI Impact Assessment for new system | Yes — scripts/ai_impact_assessment_checker.py |

| Building AIMS from scratch | Use ra-qm-team/iso42001-ai-management |

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ISO 42001 audit structure

Stage 1 (documentation review)

• Auditor reviews AIMS documentation (typically 1-3 days)
• Confirms scope, applicability, key documents present
• Identifies gaps before Stage 2
• Typically 2-3 weeks before Stage 2

Stage 2 (operational assessment)

• Auditor onsite (or remote) verifies AIMS operating effectively
• 3-10 days depending on scope + system count
• Walkthroughs, interviews, evidence sampling
• Conclusion: certification recommended (subject to non-conformity closure) or not

Surveillance audits

• Annual; reduced scope vs initial
• Typically 1-3 days
• Focus on high-risk areas + changes since prior audit

Re-certification (year 3)

• Full audit; similar to initial
• Typically every 3 years

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AIMS audit-prep sprint

4-week sprint (mature AIMS, surveillance audit)

Week 1: Internal audit + gap analysis
Week 2: Remediation + AI inventory refresh
Week 3: Documentation review + walkthrough rehearsal
Week 4: Auditor onsite

8-week sprint (Stage 1 + Stage 2 initial certification)

Weeks 1-3: AIMS documentation completion (Annex A controls coverage)
Weeks 4-5: Stage 1 audit + gap closure
Weeks 6-7: Stage 2 operational evidence prep + mock walkthroughs
Week 8: Stage 2 audit

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ISO 42001 clauses + Annex A controls

Clauses (4-10): management system

| Clause | Topic |

|--------|-------|

| 4 | Context of the organization |

| 5 | Leadership |

| 6 | Planning (including AI risk + AI objectives) |

| 7 | Support (resources, competence, awareness, communication, documentation) |

| 8 | Operation (AI lifecycle, supplier relationships) |

| 9 | Performance evaluation (monitoring, internal audit, management review) |

| 10 | Improvement (nonconformity, continual improvement) |

Annex A controls (10 areas)

| Annex A area | Topics |

|--------------|--------|

| A.2 | Policies related to AI |

| A.3 | Internal organization |

| A.4 | Resources for AI systems |

| A.5 | Assessing impacts of AI systems |

| A.6 | AI system lifecycle |

| A.7 | Data for AI systems |

| A.8 | Information for interested parties |

| A.9 | Use of AI systems |

| A.10 | Third-party relationships |

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Critical audit areas

AI Inventory + Impact Assessments

| Item | Evidence | Common gap |

|------|----------|------------|

| Complete AI system inventory | Inventory document | Shadow AI not captured |

| AI Impact Assessment (AIIA) per system | Per-system AIIA | Skipped for "low-risk" systems |

| AIIA reviewed periodically | Review records | One-time only |

| Risk classification of systems | Per system | Not documented |

AI Policy + Governance

| Item | Evidence | Common gap |

|------|----------|------------|

| AI policy approved + dated | Signed policy | Not signed / stale |

| AI ethics principles | Documented principles | Generic; not actionable |

| AI governance body | Charter / minutes | Not formalized |

| Roles + responsibilities | RACI | Not defined |

AI Lifecycle Management (Annex A.6)

| Item | Evidence | Common gap |

|------|----------|------------|

| AI development lifecycle defined | Process documentation | Not formalized |

| Data quality controls | Per system | Generic only |

| Model validation procedures | Per system | Validation skipped |

| AI system testing | Per system | Inadequate testing |

| Deployment controls | Per system | No controls |

| Operational monitoring | Per system | Drift not monitored |

| Decommissioning procedures | Per system | Not defined |

Data Governance (Annex A.7)

| Item | Evidence | Common gap |

|------|----------|------------|

| Data sources documented | Per system | Vague |

| Data quality assessed | Quality metrics | Not measured |

| Data lineage tracked | Documentation | Untracked |

| Sensitive data protection | Controls | Insufficient |

Third-party AI (Annex A.10)

| Item | Evidence | Common gap |

|------|----------|------------|

| Third-party AI inventory | List | Incomplete |

| Vendor due diligence for AI | Per vendor | Generic IT only |

| Contract terms for AI vendors | AI-specific clauses | Standard MSA only |

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Clarify First

Before running the audit-prep, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• [ ] Audit type and stage — Stage 1 documentation review, Stage 2 operational, surveillance, or internal (sets sprint length and whether the focus is documentation or operational evidence)
• [ ] AIMS maturity — mature system vs building from gaps (picks the 4-week vs 8-week sprint)
• [ ] AI systems in scope — which systems and how many (drives the AIIA count and Annex A coverage)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the readiness assessment.

Quick start

1. Run readiness score: python3 scripts/aims_readiness_score.py --config aims-controls.yaml
2. Check AIIA per system: python3 scripts/ai_impact_assessment_checker.py --aiia system-aiia.yaml
3. Pick sprint length based on score
4. Execute sprint per [references/iso42001-aims-readiness-checklist.md](references/iso42001-aims-readiness-checklist.md)

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Common AIMS audit findings

• AI inventory incomplete — shadow AI not captured
• AIIA missing for systems that look "small" but have impact
• AI lifecycle process not implemented (designed only)
• Data governance generic — not AI-specific
• Performance monitoring missing — drift not detected
• AI policy stale — written before current AI deployment
• Third-party AI vendors not assessed
• Continual improvement not evidenced

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Tooling

| Script | Purpose |

|--------|---------|

| scripts/aims_readiness_score.py | Score AIMS readiness per clause + Annex A area |

| scripts/ai_impact_assessment_checker.py | Validate AI Impact Assessment completeness |

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References

• [iso42001-aims-readiness-checklist.md](references/iso42001-aims-readiness-checklist.md) — punch list per clause + Annex A
• [aims-internal-audit-playbook.md](references/aims-internal-audit-playbook.md) — internal audit execution playbook

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Related skills

• ra-qm-team/iso42001-ai-management — deep ISO 42001 AIMS program management
• ra-qm-team/eu-ai-act-specialist — EU AI Act regulatory companion
• ra-qm-team/audit-prep/ai-act-readiness — AI Act audit-prep variant
• ra-qm-team/audit-prep/compliance-readiness — multi-framework readiness