iso42001-specialist – skill Bảo mật cho agent AI

ISO/IEC 42001 AI Management System Specialist

Internal-audit-grade operating skill for ISO/IEC 42001:2023. Three decisions, no executive AI strategy:

1. Where are the AIMS gaps against Clauses 4–10? — coverage scoring per clause + remediation priority

2. What's the AI risk register, and which controls treat each risk? — Annex A.2–A.10 control mapping per ISO 23894 risk method

3. What's the Clause 9.2 internal audit plan? — 12-month schedule with scope, frequency, auditor independence checks

This skill is NOT a chief-ai-officer-advisor replacement. CAIO decides whether to build/buy a model and what business risk to accept. This skill operates the management-system discipline that captures those decisions in audit-ready evidence.

This skill is NOT an EU AI Act compliance skill. ISO 42001 is a voluntary management-system standard; EU AI Act is binding product-safety regulation. They overlap (a high-risk AI system per Article 6(2) of the AI Act typically requires the QMS in Article 17, which ISO 42001 can satisfy in part) but the artefacts differ. See compliance-team-eu-ai-act for Article-level conformity assessment.

This skill is NOT a substitute for ISO 23894 + 38507. 42001 is the management system; 23894 is the AI risk methodology that feeds Clause 6.1; 38507 is the governance lens. The ai_risk_register_builder.py tool implements the 23894 process; treat the references as the methodology bridge.

Keywords

ISO 42001, ISO/IEC 42001:2023, AI Management System, AIMS, AI governance, AI risk management, ISO 23894, AI risk assessment, ISO 38507, AI compliance, AI audit, internal audit AI, Annex A controls, AI risk register, AI policy, AI impact assessment, conformity declaration, AI lifecycle, AI risk treatment, NIST AI RMF, NIST AI Risk Management Framework, ISACA AI audit, BSI AIC4, AI assurance, responsible AI, AI ethics governance, AI system inventory, third-party AI risk, AI vendor management, AI change management, AI incident management

Quick Start

# Decision A: AIMS gap analysis against Clauses 4-10
python scripts/aims_gap_analyzer.py                           # embedded sample (mid-stage AI SaaS)
python scripts/aims_gap_analyzer.py path/to/aims_evidence.json

# Decision B: AI risk register + Annex A control mapping
python scripts/ai_risk_register_builder.py                    # embedded 7-risk sample
python scripts/ai_risk_register_builder.py path/to/risks.json

# Decision C: Clause 9.2 internal audit 12-month plan
python scripts/aims_audit_scheduler.py                        # embedded 4-domain sample
python scripts/aims_audit_scheduler.py path/to/scope.json

Key Questions (ask these first)

• Does the AIMS scope statement (Clause 4.3) name every AI system, including embedded models and third-party AI services? If "AI features added by our SaaS vendors" is not in scope, the AIMS is incomplete.
• Does the AI policy (Clause 5.2) commit to lawful use AND beneficial purpose AND human oversight AND continual improvement? Missing any of the four = nonconformity at certification.
• Has the AI risk assessment (Clause 6.1.2) been re-run since the last material model change? Concept drift is not a one-time event.
• Who signs the AI impact assessment for high-impact systems (Annex A.5.4)? If no signed accountability, the control is missing.
• What's the internal audit cadence (Clause 9.2)? ISO management-system standards expect ≥ once per 3-year cycle per clause; mature programs do annual.
• Is there a documented procedure for AI incidents (Annex A.9.3)? Untreated post-deployment monitoring is the #1 nonconformity in early adopters.

Core Responsibilities

1. AIMS Gap Analysis (Clauses 4–10)

The framework: ISO 42001 follows the Annex SL high-level structure shared with ISO 9001 / 27001 / 13485. Clauses 4–10 are the management-system requirements; Annex A controls A.1–A.10 are the AI-specific operational controls.

| Clause | What it requires | Common gap |

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

| 4. Context | AI scope, interested parties, external context | Scope omits third-party AI services |

| 5. Leadership | AI policy, roles, accountability | Policy treats "AI ethics" as marketing copy, not commitment |

| 6. Planning | AI risk + impact assessment, objectives | Risk register doesn't link to controls |

| 7. Support | Resources, competence, awareness, documented info | Competence requirements undefined for ML engineers |

| 8. Operation | Operational planning, AI system lifecycle | Lifecycle stages not mapped to Annex A controls |

| 9. Performance | Monitoring, internal audit, management review | Drift monitoring exists in code but not in management review inputs |

| 10. Improvement | Nonconformity, corrective action, continual improvement | CAPA loop separate from existing 13485/9001 CAPA — duplication |

Run aims_gap_analyzer.py with an evidence inventory JSON to score each clause (full / partial / missing) and get a prioritized remediation list.

See references/iso42001_clauses.md for the full clause-by-clause walkthrough with audit evidence expectations.

2. AI Risk Register + Annex A Control Mapping

The framework: Clause 6.1.2 requires AI risk assessment; Clause 6.1.3 requires risk treatment. Annex A provides 38 controls organized into 10 control categories (A.2–A.10). The risk register must show each identified risk linked to ≥ 1 control that treats it.

Annex A control categories (the 10):

| ID | Category | Example controls |

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

| A.2 | AI policy | A.2.2 AI policy, A.2.3 alignment with other policies |

| A.3 | Internal organization | A.3.2 AI roles & responsibilities, A.3.3 reporting concerns |

| A.4 | Resources for AI systems | A.4.2 data resources, A.4.3 tooling, A.4.4 human resources |

| A.5 | Assessing impacts | A.5.2 AI system impact assessment, A.5.4 documentation of impact assessment |

| A.6 | AI system lifecycle | A.6.2.2 objectives, A.6.2.3 lifecycle phases, A.6.2.4 verification & validation |

| A.7 | Data for AI systems | A.7.2 data management, A.7.3 data quality, A.7.4 data provenance, A.7.5 data preparation |

| A.8 | Information for interested parties | A.8.2 system documentation, A.8.3 user information, A.8.4 communication of incidents |

| A.9 | Use of AI systems | A.9.2 intended use, A.9.3 monitoring of operation, A.9.4 logging of system events |

| A.10 | Third-party & customer relationships | A.10.2 supplier relationships, A.10.3 customer relationships |

ISO/IEC 23894:2023 provides the AI-specific risk-management process (the methodology); 42001 Annex A provides the controls. The risk register is the bridge.

Run ai_risk_register_builder.py with an identified-risks JSON to produce a structured register with mapped controls + residual-risk verdict per ISO 23894 risk-treatment options.

See references/aims_controls_annex_a.md for the full 38-control catalogue with audit evidence per control.

3. Clause 9.2 Internal Audit Plan

The framework: Clause 9.2 requires "internal audits at planned intervals to provide information on whether the AIMS conforms to the organization's requirements and is effectively implemented and maintained." That's the management-system requirement; the how often and how deep are organizational choices.

Mature-program defaults:

• Cover every clause + every applicable Annex A control over a 3-year cycle (rolling)
• Annual full-system audit covering Clauses 4, 5, 9, 10 (the "always relevant" clauses)
• Quarterly or semi-annual deep dives on Clauses 6, 7, 8 by domain (per AI system or per lifecycle phase)
• Auditor independence: nobody audits their own work; A.6 lifecycle owner cannot audit Clause 8 operation

Run aims_audit_scheduler.py with a scope JSON (AI systems in scope, prior-year findings, certification cycle phase) to produce a 12-month plan with auditor assignments and independence checks.

See references/aims_implementation_guide.md for the maturity model and rollout sequencing (year 1 establish, year 2 certify, year 3+ continual improvement).

Workflows

Workflow 1: AIMS Gap Closure for Certification (4–8 weeks)

Goal: Identify gaps; prioritize remediation; close before stage 1 certification audit.

# 1. Inventory current AIMS evidence (policies, procedures, records)
python scripts/aims_gap_analyzer.py aims_evidence.json
# 2. Review gap matrix; group by clause
# 3. For each gap, identify owner + due date (target: close before stage 1)
# 4. Cross-check against ISO 27001 / 13485 existing artifacts — many can be reused
# 5. Cross-check against EU AI Act obligations (use compliance-team-eu-ai-act)
# 6. Output: prioritized remediation plan with owners + dates

Workflow 2: AI Risk Register Build (1–2 weeks)

Goal: Construct the Clause 6.1.2 risk register with full Annex A control coverage.

# 1. Run ISO 23894 risk identification across AI lifecycle (d

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