Training Module
Auditing AI Lifecycle & Data Governance Controls
Evaluate lifecycle and data governance controls across data sourcing, training, validation, deployment, monitoring, and change in ISO/IEC 42001
Overview
Auditing an AI management system becomes unreliable when lifecycle evidence is fragmented: data provenance is unclear, training and validation decisions cannot be reproduced, deployments bypass change control, and monitoring fails to detect drift. In practice, this creates false assurance: controls exist, but they do not govern what actually happens across the AI lifecycle.
This standard-specific auditing module shows how to audit lifecycle and data governance controls in an ISO/IEC 42001 context: what to look for, where evidence typically sits, how to connect lifecycle stages, and how to judge effectiveness under change. It is designed to stand on its own in the ISO/IEC 42001 auditor pathway. Generic audit craft and generic management-system methods are assumed and briefly referenced rather than retaught.
Applicable environments
This module focuses on auditing clauses and controls that are specific to ISO/IEC 42001. It is intended for auditors working with organisations operating an AI management system (AIMS) according to this standard.
Target audience
Aspiring auditors who want to audit AI management systems against ISO/IEC 42001 following best practices
Practising ISO/IEC 42001 auditors who want to strengthen their audit knowledge, judgement, and effectiveness
Decision support
Is this module for you?
Agenda
Auditing the AI lifecycle in practice
Data sourcing and provenance controls
Training and validation controls
Deployment and change control
Monitoring, drift, and operational oversight
Lifecycle governance and accountability evidence
Case-based audit simulation
Show detailed agenda...
Learning outcomes
Key outcomes
Trace an AI system from data sourcing through training, validation, deployment, and monitoring using lifecycle audit trails
Identify lifecycle-stage evidence sources and evaluate whether they are coherent, complete, and usable
Judge control effectiveness under change (version updates, data updates, configuration changes, and operational drift)
Additional capabilities
Distinguish isolated control lapses from systemic lifecycle governance weaknesses
Recognise common lifecycle and data governance failure modes that lead to “false assurance” in AI controls
Form a defensible audit view on whether oversight mechanisms are operating as intended across the lifecycle
Materials
Learning materials
Slide deck
Participant workbook
Templates & tools
Practical, reusable artefacts to apply the module directly to your organisation.
Audit interview planning tool
Documented information checklist
Sampling tool
Audit analysis worksheets
Failure pattern library
Supporting AI prompt set
Confirmation
Certificate of completion
Module ID
HAM-AI-A-02
Discipline
ISO standard
Standard clause
8: Operation
Target audience
Public delivery
Live virtual
Duration
7 h
List price
CHF 550
Excl. VAT. VAT may apply depending on customer location and status.
Delivery
Live virtual delivery
This module is delivered live online and combines conceptual framing, discussion, case work and direct interaction with the instructor.
A public cohort is currently not scheduled. If you register your interest, we will notify you when a new public cohort is scheduled or suitable delivery options become available.
Custom delivery options
For organisations with specific constraints or learning objectives, the module can be adapted in format or scope, including in-house delivery and contextualised case material.
For an optimal learning experience
Prerequisites & preparation
This module is designed as part of a modular training approach. Topics are deliberately distributed across modules and are not repeated in full, in order to avoid unnecessary redundancy. Each module is self-contained and can be taken on its own. Where prior knowledge or experience is helpful, this is indicated below so you can decide whether any preparation is useful for you.
Assumed background
This module assumes auditors can already operate within an audit assignment and apply evidence-based judgement. It also assumes basic AI lifecycle literacy (common artefacts, versioning concepts, and what “drift” means operationally).
Helpful background includes:
Evidence logic, sampling judgement, and adequacy vs effectiveness thinking
Familiarity with how documented information is structured and used as audit evidence
Basic understanding of AI system lifecycle artefacts (data sources, training runs, evaluation results, deployment versions, monitoring outputs)
Preparatory modules
Foundational modules (depending on background)
Useful if you are new to the underlying concepts or want a shared baseline before attending this module.
Supporting modules (optional)
Helpful if you want to deepen related skills, but not required to participate effectively.
