Training Module
Training Module
Training Module
Auditing AI Lifecycle & Data Governance Controls
Assess evidence and control effectiveness across data sourcing, training, validation, deployment, monitoring, and lifecycle change
Understand
Implement
Manage
Audit
Move from “AI paperwork” to lifecycle evidence that holds up under scrutiny
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.
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
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.
Target audience
Internal auditors auditing AI management system lifecycle controls
Third-party auditors (for example, auditors from certification bodies and independent assurance providers) assessing ISO/IEC 42001 conformity
Supplier and partner auditors reviewing outsourced AI development, data preparation, or operated AI services
Audit programme owners who need consistent lifecycle coverage across multiple AI use cases (as an application focus, not programme design)
Internal auditors auditing AI management system lifecycle controls
Third-party auditors (for example, auditors from certification bodies and independent assurance providers) assessing ISO/IEC 42001 conformity
Supplier and partner auditors reviewing outsourced AI development, data preparation, or operated AI services
Audit programme owners who need consistent lifecycle coverage across multiple AI use cases (as an application focus, not programme design)
Agenda
What “auditing the AI lifecycle” means in practice
Lifecycle stages as audit trails (not as a process design exercise)
Control adequacy vs control effectiveness across stages
Data sourcing and provenance controls
Typical evidence: sourcing decisions, rights/constraints, lineage, and quality gates
Red flags: unverifiable origin, unmanaged third-party data, and “unknown reuse”
Training and validation controls
Typical evidence: dataset selection rationale, reproducibility, evaluation records, and approval points
Common breakdowns: unmanaged experiment sprawl, inconsistent validation, and undocumented model selection
Deployment and change control
Typical evidence: release decisions, versioning, rollback readiness, and segregation of duties
Third-party and outsourced patterns: what changes when the model or platform is externally operated
Monitoring, drift, and operational oversight
Typical evidence: monitoring design intent vs operation, alert handling, incidents, and corrective actions
Drift patterns: data drift, performance drift, and “silent” changes in the operating environment
Lifecycle governance and accountability evidence
Decision records: who approved what, based on which evidence, and under which constraints
Oversight mechanisms: how exceptions, emergency changes, and unresolved issues are handled
Workshop: lifecycle control effectiveness mini-audit (case-based)
Map an end-to-end lifecycle audit trail for a provided AI use case
Identify the minimum evidence set to support an effectiveness judgement (including third-party auditor perspective)
What “auditing the AI lifecycle” means in practice
Lifecycle stages as audit trails (not as a process design exercise)
Control adequacy vs control effectiveness across stages
Data sourcing and provenance controls
Typical evidence: sourcing decisions, rights/constraints, lineage, and quality gates
Red flags: unverifiable origin, unmanaged third-party data, and “unknown reuse”
Training and validation controls
Typical evidence: dataset selection rationale, reproducibility, evaluation records, and approval points
Common breakdowns: unmanaged experiment sprawl, inconsistent validation, and undocumented model selection
Deployment and change control
Typical evidence: release decisions, versioning, rollback readiness, and segregation of duties
Third-party and outsourced patterns: what changes when the model or platform is externally operated
Monitoring, drift, and operational oversight
Typical evidence: monitoring design intent vs operation, alert handling, incidents, and corrective actions
Drift patterns: data drift, performance drift, and “silent” changes in the operating environment
Lifecycle governance and accountability evidence
Decision records: who approved what, based on which evidence, and under which constraints
Oversight mechanisms: how exceptions, emergency changes, and unresolved issues are handled
Workshop: lifecycle control effectiveness mini-audit (case-based)
Map an end-to-end lifecycle audit trail for a provided AI use case
Identify the minimum evidence set to support an effectiveness judgement (including third-party auditor perspective)
Module ID
HAM-AI-A-02
Module type
Audit Add-on
Domain:
Artificial Intelligence
Audience:
Auditor
Available in:
English
Duration:
3.5 h
List price:
CHF 275
Excl. VAT. VAT may apply depending on customer location and status.
What you get
Learning 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)
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
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)
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
Learning materials
Slide deck
Participant workbook
Certificate of completion
Slide deck
Participant workbook
Certificate of completion
Templates & tools
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)
Prerequisites
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)
Strongly recommended preparatory modules
Audit Foundations: Principles, Evidence & Judgement
Core audit mindset, evidence logic, materiality-based focus, and audit test plan design.
7 h
Audit Foundations: Principles, Evidence & Judgement
Core audit mindset, evidence logic, materiality-based focus, and audit test plan design.
7 h
Audit Foundations: Principles, Evidence & Judgement
Core audit mindset, evidence logic, materiality-based focus, and audit test plan design.
7 h
AI System Scope, Lifecycle & Inventory (ISO/IEC 42001)
Define AI system scope, lifecycle boundaries, and a maintained AI system inventory aligned to ISO/IEC 42001
7 h
AI System Scope, Lifecycle & Inventory (ISO/IEC 42001)
Define AI system scope, lifecycle boundaries, and a maintained AI system inventory aligned to ISO/IEC 42001
7 h
AI System Scope, Lifecycle & Inventory (ISO/IEC 42001)
Define AI system scope, lifecycle boundaries, and a maintained AI system inventory aligned to ISO/IEC 42001
7 h
ISO/IEC 42001: 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
ISO/IEC 42001: 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
ISO/IEC 42001: 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
ISO/IEC 42001: Operational Control of AI Systems
Understand how to define, implement, and maintain operational controls for AI systems across deployment, change, and monitoring
7 h
ISO/IEC 42001: Operational Control of AI Systems
Understand how to define, implement, and maintain operational controls for AI systems across deployment, change, and monitoring
7 h
ISO/IEC 42001: Operational Control of AI Systems
Understand how to define, implement, and maintain operational controls for AI systems across deployment, change, and monitoring
7 h
Helpful preparatory modules
The modules below prepare for an optimal learning experience – but are not strictly necessary for participants to follow.
AI Fundamentals I: AI Concepts & System Types
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: AI Concepts & System Types
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: AI Concepts & System Types
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: AI Limitations, Uncertainty & Failure Modes
Understand AI uncertainty, limitations, and common failure modes across predictive and generative AI systems
7 h
AI Fundamentals II: AI Limitations, Uncertainty & Failure Modes
Understand AI uncertainty, limitations, and common failure modes across predictive and generative AI systems
7 h
AI Fundamentals II: AI Limitations, Uncertainty & Failure Modes
Understand AI uncertainty, limitations, and common failure modes across predictive and generative AI systems
7 h
Conducting Confident, Objective, and Insightful Audit Conversations
Interview planning, questioning, and conversation control for reliable audit evidence
7 h
Conducting Confident, Objective, and Insightful Audit Conversations
Interview planning, questioning, and conversation control for reliable audit evidence
7 h
Conducting Confident, Objective, and Insightful Audit Conversations
Interview planning, questioning, and conversation control for reliable audit evidence
7 h
Audit Reporting & Follow-up: Findings, Reporting, and Verification of Closure
Understand how to write evidence-based findings, structure audit reports, and follow up agreed actions to verified closure.
7 h
Audit Reporting & Follow-up: Findings, Reporting, and Verification of Closure
Understand how to write evidence-based findings, structure audit reports, and follow up agreed actions to verified closure.
7 h
Audit Reporting & Follow-up: Findings, Reporting, and Verification of Closure
Understand how to write evidence-based findings, structure audit reports, and follow up agreed actions to verified closure.
7 h
Continuous learning
Continuous learning
Continuous learning
Follow-up modules
Follow-up modules
After completion of this module, the following modules are ideal to further deepen your competence.
After completion of this module, the following modules are ideal to further deepen your competence.
AI Risk, Impact & Harm Assessment
ISO/IEC 42001: AI Risk, Impact & Harm Assessment
Duration
7 h
List price
CHF 550
View module
AI Risk, Impact & Harm Assessment
ISO/IEC 42001: AI Risk, Impact & Harm Assessment
Duration
7 h
List price
CHF 550
View module
AI Risk, Impact & Harm Assessment
ISO/IEC 42001: AI Risk, Impact & Harm Assessment
Duration
7 h
List price
CHF 550
View module
Ready to achieve mastery?
Bring ISO requirements into everyday practice to reduce avoidable issues and strengthen the trust of your customers and stakeholders.
Ready to achieve mastery?
Bring ISO requirements into everyday practice to reduce avoidable issues and strengthen the trust of your customers and stakeholders.
Ready to achieve mastery?
Bring ISO requirements into everyday practice to reduce avoidable issues and strengthen the trust of your customers and stakeholders.