Training Module
Auditing AI Lifecycle & Data Governance Controls
Assess evidence and control effectiveness across data sourcing, training, validation, deployment, monitoring, and lifecycle change
Training Module
Auditing AI Lifecycle & Data Governance Controls
Assess evidence and control effectiveness across data sourcing, training, validation, deployment, monitoring, and lifecycle change
Training Module
Auditing AI Lifecycle & Data Governance Controls
Assess evidence and control effectiveness across data sourcing, training, validation, deployment, monitoring, and lifecycle change
Move from “AI paperwork” to lifecycle evidence that holds up under scrutiny
AI controls often look complete on paper but fail when traced through data origin, model changes, deployments, and monitoring. This module equips auditors to follow lifecycle audit trails, judge control effectiveness, and spot drift and oversight gaps early enough to matter.
Move from “AI paperwork” to lifecycle evidence that holds up under scrutiny
AI controls often look complete on paper but fail when traced through data origin, model changes, deployments, and monitoring. This module equips auditors to follow lifecycle audit trails, judge control effectiveness, and spot drift and oversight gaps early enough to matter.
Move from “AI paperwork” to lifecycle evidence that holds up under scrutiny
AI controls often look complete on paper but fail when traced through data origin, model changes, deployments, and monitoring. This module equips auditors to follow lifecycle audit trails, judge control effectiveness, and spot drift and oversight gaps early enough to matter.
Training module overview
Training module overview
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 audit add-on focuses on 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 does not teach generic audit craft or generic management-system methods; those are assumed and referenced.
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 audit add-on focuses on 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 does not teach generic audit craft or generic management-system methods; those are assumed and referenced.
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
Target audience
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
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
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.
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
Agenda
Agenda
What “auditing the AI lifecycle” means 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...
What “auditing the AI lifecycle” means 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...
What “auditing the AI lifecycle” means 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
Learning outcomes
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)
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
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
Additional benefits
Additional benefits
Learning materials
Slide deck
Participant workbook
Templates & tools
Practical, reusable artefacts to apply the module directly to your organisation.
AI lifecycle audit-trail map (stage-to-evidence linkage)
Evidence checklist by lifecycle stage (data / training / validation / deployment / monitoring)
Drift and change “red flags” library (what to test, where to look, what often gets missed)
Third-party lifecycle evidence request list (for suppliers, platforms, and managed services)
Lifecycle coverage and sampling cues (capability-specific, not generic audit sampling theory)
AI lifecycle audit-trail map (stage-to-evidence linkage)
Evidence checklist by lifecycle stage (data / training / validation / deployment / monitoring)
Drift and change “red flags” library (what to test, where to look, what often gets missed)
Third-party lifecycle evidence request list (for suppliers, platforms, and managed services)
Lifecycle coverage and sampling cues (capability-specific, not generic audit sampling theory)
Confirmation
Certificate of completion
Module ID
HAM-AI-A-02
Domain
Audience
Auditor
Language
English
Delivery
Live virtual
Duration
3.5 h
List price
CHF 275
Excl. VAT. VAT may apply depending on customer location and status.
Delivery & learning format
Delivery & learning format
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.
Not sure if this module is right for you?
Not sure if this module is right for you?
Not sure if this module is right for you?
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.
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.
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)
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.
Audit Foundations
Understand core audit mindset, evidence logic, materiality-based focus, and audit test plan design
7 h
Audit Foundations
Understand core audit mindset, evidence logic, materiality-based focus, and audit test plan design
7 h
Audit Foundations
Understand core audit mindset, evidence logic, materiality-based focus, and audit test plan design
7 h
AI System Scope, Lifecycle & Inventory
Define AI system scope, lifecycle boundaries, and a maintained AI system inventory aligned to ISO/IEC 42001
7 h
AI System Scope, Lifecycle & Inventory
Define AI system scope, lifecycle boundaries, and a maintained AI system inventory aligned to ISO/IEC 42001
7 h
AI System Scope, Lifecycle & Inventory
Define AI system scope, lifecycle boundaries, and a maintained AI system inventory aligned to ISO/IEC 42001
7 h
AI Risk, Impact & Harm Assessment
Understand how to assess AI impacts and harms, document results, and connect them to risk decisions in an AI management system
7 h
AI Risk, Impact & Harm Assessment
Understand how to assess AI impacts and harms, document results, and connect them to risk decisions in an AI management system
7 h
AI Risk, Impact & Harm Assessment
Understand how to assess AI impacts and harms, document results, and connect them to risk decisions in an AI management system
7 h
Operational Control of AI Systems
Understand how to define, implement, and maintain operational controls for AI systems across deployment, change, and monitoring
7 h
Operational Control of AI Systems
Understand how to define, implement, and maintain operational controls for AI systems across deployment, change, and monitoring
7 h
Operational Control of AI Systems
Understand how to define, implement, and maintain operational controls for AI systems across deployment, change, and monitoring
7 h
Supporting modules (optional)
Helpful if you want to deepen related skills, but not required to participate effectively.
AI Fundamentals I
Learn core AI concepts, AI system types, and the technical building blocks that underpin modern AI-enabled products and services
7 h
AI Fundamentals I
Learn core AI concepts, AI system types, and the technical building blocks that underpin modern AI-enabled products and services
7 h
AI Fundamentals I
Learn core AI concepts, AI system types, and the technical building blocks that underpin modern AI-enabled products and services
7 h
AI Fundamentals II
Understand AI uncertainty, limitations, and common failure modes across predictive and generative AI systems
7 h
AI Fundamentals II
Understand AI uncertainty, limitations, and common failure modes across predictive and generative AI systems
7 h
AI Fundamentals II
Understand AI uncertainty, limitations, and common failure modes across predictive and generative AI systems
7 h
Audit Execution: Communication & Interviewing
Learn the skills for effective interview planning, questioning, and conversation control for reliable audit evidence
7 h
Audit Execution: Communication & Interviewing
Learn the skills for effective interview planning, questioning, and conversation control for reliable audit evidence
7 h
Audit Execution: Communication & Interviewing
Learn the skills for effective interview planning, questioning, and conversation control for reliable audit evidence
7 h
Audit Reporting & Follow-up
Understand how to write evidence-based findings, structure audit reports, and follow up agreed actions to verified closure
7 h
Audit Reporting & Follow-up
Understand how to write evidence-based findings, structure audit reports, and follow up agreed actions to verified closure
7 h
Audit Reporting & Follow-up
Understand how to write evidence-based findings, structure audit reports, and follow up agreed actions to verified closure
7 h
Continuous learning
Follow-up modules
Continuous learning
Follow-up modules
Continuous learning
Follow-up modules
After completion of this module, the following modules are ideal to further deepen your competence. If you are looking for a structured learning path, modules can also be taken as part of a professional track.
Ready to improve your management systems?
We support continuous improvement by embedding ISO requirements into everyday practice and daily operations.
Ready to improve your management systems?
We support continuous improvement by embedding ISO requirements into everyday practice and daily operations.
Ready to improve your management systems?
We support continuous improvement by embedding ISO requirements into everyday practice and daily operations.