ISO/IEC 42001 AI Management System Training
Practical, role-based training for building and governing a certifiable AI Management System (AIMS) — aligned to the EU AI Act, the NIST AI RMF, and the standard that now anchors responsible AI programs worldwide.
Four modules, built around who touches the risk
ISO/IEC 42001 Foundations — the AI Management System and the Global AI Regulatory Landscape
What ISO/IEC 42001 is, why it exists, and how it fits alongside the EU AI Act and the NIST AI RMF — the shared foundation every role needs.
AI Governance & Implementation Team
Building the risk assessments, controls, and lifecycle documentation that make an AIMS operate day to day.
Internal Auditors (AI Management Systems)
Auditing an AIMS against ISO/IEC 42001 — evidence, non-conformities, and certification-body readiness.
Top Management & AI Governance Leadership — with Extensive Regulatory Strategy
Board-level accountability, cross-jurisdiction regulatory strategy, and continual improvement, covered in depth.
ISO/IEC 42001 Foundations — the AI Management System and the Global AI Regulatory Landscape
Published in December 2023, ISO/IEC 42001 is the world's first international standard specifying requirements for an artificial intelligence management system — a certifiable, auditable framework for how an organization governs the AI it builds, buys, or deploys. It follows the same high-level structure (Annex SL) as ISO/IEC 27001, so organizations already certified to a management-system standard will recognize the shape: a Plan-Do-Check-Act cycle of leadership commitment, risk assessment, defined controls, and continual improvement, now applied specifically to AI. What makes it distinctive is its Annex A control set — 38 controls spanning AI policy, impact assessment, data quality, human oversight, and third-party AI relationships. As binding AI regulation arrives in phases across the EU, UK, US states, and elsewhere, ISO/IEC 42001 has become the practical operating system organizations use to demonstrate — to regulators, customers, and their own boards — that AI risk is actually being managed, not just discussed.
Core Risk Areas Covered
AI Risk & Impact Assessment
Identifying and evaluating the risks a given AI system poses to individuals, groups, and the organization before and during deployment.
Data Quality & Bias Management
Governing the data that trains and feeds AI systems, including provenance, quality, and bias testing across the lifecycle.
Human Oversight of Automated Decisions
Designing meaningful human review and intervention points into systems that make or inform consequential decisions.
Third-Party & Vendor AI Model Risk
Assessing the risk introduced by foundation models, APIs, and AI vendors the organization does not fully control.
AI Incident Management
Detecting, escalating, and responding to AI system failures, harmful outputs, and near-misses in a structured, auditable way.
Transparency & Explainability Documentation
Maintaining the records — model cards, decision logs, impact assessments — that regulators and auditors will ask to see.
The Regulatory Landscape, Explained Plainly
EU AI Act (Regulation (EU) 2024/1689)
Entered into force 1 August 2024. Prohibited AI practices have applied since 2 February 2025, and obligations for general-purpose AI (GPAI) models since 2 August 2025. Following the 2026 Digital Omnibus on AI, high-risk AI system obligations now apply from 2 December 2027 for Annex III use cases (biometrics, employment, education, critical infrastructure, and more) and from 2 August 2028 for AI embedded in products already regulated under EU product-safety law (Annex I).
NIST AI Risk Management Framework (AI RMF 1.0)
Published by the U.S. National Institute of Standards and Technology on 26 January 2023. A voluntary framework organized around four functions — Govern, Map, Measure, Manage — that is increasingly used by regulators, insurers, and enterprise customers as a benchmark for trustworthy AI risk management.
ISO/IEC 42001:2023
Published December 2023 as the world's first certifiable, international management-system standard for artificial intelligence. Its Annex SL structure and 38 Annex A controls give organizations a concrete, auditable way to operationalize the risk-based principles found in the EU AI Act and the NIST AI RMF, rather than treating each regime as a separate compliance project.
Regulatory emphasis is tailored to your country and jurisdiction — tell us where you operate in the form below and we'll scope the frameworks that matter most to you.
Built Around Who Actually Touches the Risk
After the shared foundation, participants split into focused tracks — each built from the systems and decisions that group actually owns.
AI Governance & Implementation Team
AI/ML Engineering Leads · AI Governance Managers · Product Owners
Internal Auditors (AI Management Systems)
Internal Audit Staff · Quality & Compliance Teams
"Building an AI management system and auditing one require different instincts. The team implementing ISO/IEC 42001 needs to translate abstract control language into working risk assessments, data pipelines, and human-oversight checkpoints. The team auditing it needs to independently verify that those controls actually operate as documented — without being talked out of a legitimate finding by the people who built the system. Training both groups together tends to blur that independence. Training them separately, with scenarios drawn from their actual day-to-day decisions, produces implementers who document defensibly and auditors who know precisely what AI-specific evidence to demand."
AI Governance & Implementation Team — Scenarios
Internal Auditors (AI Management Systems) — Scenarios
Format for These Tracks
Top Management & AI Governance Leadership
Board Members · Executives · Chief AI/Risk Officers
ISO/IEC 42001 puts specific, non-delegable obligations on top management — leadership commitment, resourcing, and accountability for the AI management system are clauses in the standard itself, not optional extras. This module is built for the people who carry that accountability: boards, executives, and the leaders who will answer for AI governance maturity in a customer due-diligence questionnaire, a regulator's inquiry, or a certification audit. It goes deep on the regulatory strategy question every leadership team now faces — how to align a single AIMS with the EU AI Act's staggered, recently-revised deadlines, the NIST AI RMF, and whatever comes next — so that governance decisions made today hold up as the landscape keeps shifting.
Covered in Depth
- Instructor-led workshops
- Self-paced e-learning modules
- Scenario & tabletop exercises
- Role-specific refreshers
All four modules can be delivered together as a full program, or the role-specific tracks can be licensed independently for organisations that only need one audience covered.
The stakes behind the training
One Framework, Every AI Use Case
ISO/IEC 42001 doesn't ask you to build a separate governance program for each AI application — the same AIMS scales from a single internal chatbot to enterprise-wide model deployment.
Certification-Ready, Not Just Awareness
This training is built around the actual clause and Annex A structure your certification body will audit against, not a general-purpose introduction to 'responsible AI'.
Built for the People Who Own the Risk
Implementers, auditors, and leadership each get training scoped to the decisions and evidence their role is actually accountable for.
Bring ISO/IEC 42001 training to your AI governance program.
Schedule a consultation to scope the right modules for your teams. Tell us your country and we'll tailor the regulatory focus and delivery format to your region.
Modular by design
Run the full four-module program, or license just the tracks your organisation needs — built on the shared foundation module.
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