ISO/IEC 42001:2023 Artificial Intelligence Management Systems Lead Auditor Training and Certification Program

Programme Summary

Develop the planning, evidence evaluation and team leadership skills needed for AI management audits against ISO/IEC 42001:2023. An enterprise AI service deployment case connects AI governance, lifecycle risks and impact assessment evidence with realistic audit trails. Participants lead simulated meetings, evaluate conflicting evidence and defend findings, producing a clear audit conclusion while respecting impartiality, confidentiality and the limits of sampled evidence.

Training Objectives

1 Strengthen AI management audit leadership; 2 Plan defensible assignments and team responsibilities; 3 Evaluate conformity and system effectiveness; 4 Manage findings communication and audit conclusions; 5 Maintain ethical conduct and audit independence.

Learning Outcomes

1 Lead a simulated AI management audit; 2 Evaluate AI control applicability records; 3 Direct risk based sampling and interviews; 4 Defend findings and evidence limitations; 5 Recommend proportionate corrective action follow up.

Target Audience

Audit Team Leaders; Management System Auditors; AI Governance Managers; AI Product Managers; Assurance Managers.

Target Industries

Enterprise Software; Financial Services; Healthcare Technology; Government Digital Services; Industrial Automation.

Course Contents

• Module 1 Audit Leadership and Professional Boundaries: Audit principles impartiality and confidential information; First second and third party distinctions; Auditor competence and certification decision boundaries.
• Module 2 Audit Programme and Assignment Risk: Programme objectives and changing operational risks; Scope criteria resources and team competence; Remote methods feasibility and evidence security.
• Module 3 Auditing AI Roles Context and System Scope: AI provider developer and user roles; System inventory and intended use context; Interested parties and organisational boundaries.
• Module 4 Auditing AI Leadership and Governance Planning: AI policy and accountable responsibilities; Objectives risks and management resources; Competence communication and documented information.
• Module 5 Auditing AI Risk Assessment and Treatment: Risk criteria and lifecycle risk scenarios; Risk analysis evaluation and treatment ownership; Control selection and applicability justification.
• Module 6 Auditing AI System Impact Assessment: Affected people groups and societal contexts; Intended use foreseeable misuse and consequences; Impact assessment evidence and review triggers.
• Module 7 Auditing AI Lifecycle Controls and Accountability: Design development and deployment responsibilities; Verification validation and operational acceptance; Human oversight and controlled lifecycle changes.
• Module 8 Auditing Data and Information Governance: Data provenance suitability and quality; Data preparation access and lifecycle controls; Documentation transparency and stakeholder information.
• Module 9 Auditing AI Suppliers Use and Monitoring: Supplier responsibilities and acquisition interfaces; Responsible use and deployment safeguards; Performance monitoring incident reporting and escalation.
• Module 10 Auditing AI Assurance and Improvement: Management system and control effectiveness measures; Internal audit and management review; Corrective action and changing AI risks.
• Module 11 Document Review and Readiness Evaluation: Scope documents and process evidence review; Readiness concerns and additional information requests; Audit plan adjustments and communication.
• Module 12 Planning Activities and Directing the Team: Responsibilities schedules and audit trail allocation; Sampling strategy and technical expert interfaces; Team briefings progress checks and coordination.
• Module 13 Opening Meetings and Audit Communication: Confirming scope criteria and practical arrangements; Managing professional disagreement and access issues; Communicating emerging risks and material concerns.
• Module 14 Gathering and Corroborating Audit Evidence: AI risk and impact assessments; AI control applicability records; AI lifecycle and monitoring records.
• Module 15 Evidence Judgement and Escalation: Evidence of unassessed AI impacts; Evidence of unclear human oversight responsibilities; Sampling limitations and additional evidence decisions.
• Module 16 Developing Findings and Audit Conclusions: Linking criteria evidence and supported findings; Evaluating systemic patterns and management effectiveness; Conclusions within scope and evidence limitations.
• Module 17 Reporting and Closing the Audit: Balanced report structure and clear findings; Closing meeting leadership and disagreement records; Confidential distribution and controlled audit records.
• Module 18 Corrective Action Review and Follow Up: Assessing cause analysis and proposed action; Verifying implementation and effectiveness evidence; Follow up escalation and unresolved findings.
• Module 19 Audit Quality and Professional Development: Reviewing audit performance and consistency; Technical competence and experience development; Learning feedback and ongoing ethical conduct.
• Module 20 Lead Audit Team Simulation: Lead assigned meetings and team briefings; Evaluate conflicting evidence and defend findings; Present conclusions and follow up recommendations.

Assessment

• 100 single best answer questions, including applied scenarios; one mark per question.
• 150 minute invigilated digital examination with identity verification. Approved accessibility adjustments preserve the assessed competence.
• Clean standard text and supplied case documents permitted; internet, messaging and external AI tools prohibited.
• Individual applied assessment (150 minutes): Lead a simulated AI management audit, assign team activities, conduct an assessed interview, evaluate conflicting records, present supported findings and defend the audit conclusion and follow up decisions.
• Assessment criteria: Defensible audit scope and sampling of AI control applicability records; Effective team direction interviewing and closing meeting leadership; Evidence linked findings conclusions and justified follow up decisions.
• Pass each component separately: 75/100 written marks (75%) and at least 70% on the applied task.
• All mandatory criteria must pass: Do not fabricate evidence or knowingly present unsupported conformity conclusions.
• Individual results and feedback are provided. One reassessment may follow coaching under the published enrolment terms.

Certification

• Successful learners receive a TQCSI Arabia digital Certificate of Successful Completion.
• The certificate records the learner name, exact programme title, teaching hours, completion date and unique verification reference.
• Verified identity, full attendance and separate written and applied passes are required; all mandatory critical criteria must be met.
• Proposed issue target: within 30 calendar days of approved results and settlement of applicable fees.
• Records successful lead auditor training in AI management; auditor registration, certification body appointment and independent personnel certification require separate evaluation.
• Attendance confirmation may record completed hours for unsuccessful learners; it does not certify successful completion.

Programme Profile

Number of daysNumber of daysNumber of days

555

Total teaching hoursTotal teaching hoursTotal teaching hours

35 hours; breaks, pre course work and formal final assessments excluded.35 hours; breaks, pre course work and formal final assessments excluded.35 hours; breaks, pre course work and formal final assessments excluded.

Delegates allowedDelegates allowedDelegates allowed

Maximum 16 delegates.Maximum 16 delegates.Maximum 16 delegates.

Training prerequisitesTraining prerequisitesTraining prerequisites

Working knowledge of ISO/IEC 42001:2023, audit fundamentals and relevant AI management experience is required. Prior internal audit practice is strongly recommended.Working knowledge of ISO/IEC 42001:2023, audit fundamentals and relevant AI management experience is required. Prior internal audit practice is strongly recommended.Working knowledge of ISO/IEC 42001:2023, audit fundamentals and relevant AI management experience is required. Prior internal audit practice is strongly recommended.

Pre course assignment and assessmentPre course assignment and assessmentPre course assignment and assessment

Complete a 45-minute requirements and audit diagnostic; review AI control applicability records and identify high risk audit trails.Complete a 45-minute requirements and audit diagnostic; review AI control applicability records and identify high risk audit trails.Complete a 45-minute requirements and audit diagnostic; review AI control applicability records and identify high risk audit trails.

End of course assessmentEnd of course assessmentEnd of course assessment

100 question written exam (150 minutes) and individual applied task (150 minutes).100 question written exam (150 minutes) and individual applied task (150 minutes).100 question written exam (150 minutes) and individual applied task (150 minutes).

Passing criteriaPassing criteriaPassing criteria

75% written and 70% applied, passed separately; full attendance. All mandatory criteria must pass.75% written and 70% applied, passed separately; full attendance. All mandatory criteria must pass.75% written and 70% applied, passed separately; full attendance. All mandatory criteria must pass.