ISO/IEC 42001:2023
Lead Auditor
A five-day professional programme designed to develop the competence to plan, conduct, report and follow up audits of an Artificial Intelligence Management System (AIMS), while evaluating AI governance, risk, impact, lifecycle controls, accountability, transparency and continual improvement.
Course Objective
Develop the capability to lead credible and evidence-based AIMS audits.
The course prepares participants to interpret ISO/IEC 42001:2023 from an auditor’s perspective, establish audit criteria, develop an audit programme and plan, lead an audit team, collect and evaluate objective evidence, assess the effectiveness of AI governance and operational controls, formulate defensible findings, report conclusions, and evaluate corrective actions and continual improvement.
Market Need
Why AI management-system auditing capability is becoming strategically important.
Why Attend?
Move beyond clause awareness to practical audit leadership.
Key Learning Areas
The essential knowledge and practical skills developed during the programme.
Detailed 5-Day Course Agenda
From AIMS interpretation to full audit simulation and reporting.
- AI governance landscape and the purpose of an AI Management System.
- Structure, terminology and intent of ISO/IEC 42001:2023.
- Context of the organization, interested parties and AIMS scope.
- Leadership, AI policy, roles, responsibilities and accountability.
- Planning: AI risks, opportunities and AIMS objectives.
- Support processes: competence, awareness, communication and documented information.
- Workshop: interpret requirements and build an initial AIMS audit criteria map.
- Operational planning and control in an AI context.
- AI risk assessment, treatment and linkage to ISO/IEC 23894 concepts.
- AI system impact assessment and evaluation of consequences to individuals, groups and society.
- AI lifecycle governance: design, development, validation, deployment, operation, monitoring and retirement.
- Data quality, data provenance, model/system documentation and traceability evidence.
- Transparency, explainability-related evidence, human oversight and responsible use.
- Third-party AI, suppliers, models, datasets, cloud AI services and outsourced dependencies.
- Workshop: develop evidence requests and test steps for selected AIMS controls.
- Auditing principles and management-system audit guidance aligned with ISO 19011.
- Audit objectives, scope, criteria, feasibility and risk-based planning.
- Audit programme design, frequency, resources and competence requirements.
- Audit team selection, team-leader responsibilities and subject-matter expertise.
- Document review, readiness assessment and development of audit trails.
- Sampling strategy, remote audit considerations and evidence reliability.
- Preparing audit plans, checklists, interview plans and working papers.
- Workshop: prepare a complete Stage-1-style readiness review and Stage-2-style audit plan.
- Opening meeting, audit communication and managing the audit team.
- Interview techniques for leadership, data teams, developers, risk, legal, HR, procurement and operations.
- Following audit trails across policy, risk, impact assessment, data, model/system lifecycle and monitoring records.
- Testing governance effectiveness rather than checking documents only.
- Assessing AI incidents, complaints, performance deviations, bias-related concerns and corrective actions.
- Evaluating control implementation, consistency, traceability and residual risk.
- Daily audit-team review, evidence reconciliation and emerging findings.
- Mock audit: interviews, evidence review, sampling and findings development.
- Conformity assessment and classification of audit findings.
- Writing clear, evidence-based nonconformities and defensible audit statements.
- Audit conclusions, management communication and closing meeting techniques.
- Audit report structure for technical, governance and executive audiences.
- Corrective action review, cause analysis, effectiveness verification and follow-up.
- Certification-audit context and awareness of ISO/IEC 42006:2025.
- Full team-based audit simulation: plan → interview → evidence → finding → report → closing meeting.
- Final evaluation, review and personal auditor development roadmap.
Lead Auditor Practical Roadmap
The end-to-end audit workflow participants will practice.
Context, criteria, scope
Trails, sampling, team
Interview, observe, verify
Findings, report, close
Corrective action, effectiveness
Practical Outputs Developed During Training
Participants leave with reusable auditor working templates and experience.
Who Should Attend?
Designed for professionals responsible for AI governance, assurance and risk oversight.
Recommended Prerequisite
Participants should have a basic understanding of management systems, governance, risk or audit. Prior ISO management-system audit experience is helpful but not mandatory. Technical AI development experience is not required; the course introduces the AI concepts necessary for management-system auditing.
Learning Outcomes
By the end of the course, participants should be able to:
Build organizational capability for trustworthy AI assurance.
Arrange a dedicated ISO/IEC 42001 Lead Auditor programme for your audit, risk, compliance, AI and technology teams.