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
Training module 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?
It is a good fit if you…
seek to audit whether AI lifecycle controls work across real system changes.
are aiming to judge data provenance, training, and validation evidence.
focus on traceability from data sourcing through deployment and monitoring.
want to audit change control and drift detection in practice.
expect to strengthen audit conclusions on AI control effectiveness.
If most of the points above apply, this module is likely a good fit.
It may not be the best fit if you…
prefer to design AI governance frameworks or lifecycle processes.
are looking for guidance on model development or data engineering.
focus primarily on AI risk management or ethical design activities.
do not intend to audit AI lifecycle controls under ISO/IEC 42001.
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
Additional benefits
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 clause
8: Operation
Audience
Auditor
Languages
English
Delivery
Live virtual
Duration
7 h
List price
CHF 550
Excl. VAT. VAT may apply depending on customer location and status.
Delivery & learning format
Virtual live teaching
This module is delivered live, with a strong focus on discussion, practical application, and direct interaction with the instructor.
Sessions work through realistic examples, clarify concepts in context, and apply methods directly to participants’ organisational realities.
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
Preparation guidance
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.
