ISO/IEC 42001:2023 Artificial Intelligence Management Lead Auditor Training

About Course

40 hours: 36 standard and audit foundation lessons, 16 advanced lead-audit lessons, four practical assignments including observed individual audit leadership, and a 40-question final exam. AED 999. W3 completion only. This training does not itself grant professional registration, accreditation or authority to certify organizations.

LO01: Interpret the subject standard and applicable audit criteria
LO02: Evaluate domain-specific evidence and performance
LO03: Manage a risk-based programme and feasible plan
LO04: Lead an independent competent audit team
LO05: Interview sample and corroborate evidence
LO06: Write and classify defensible findings
LO07: Report bounded conclusions and evaluate corrective action
LO08: Demonstrate observed leadership and verify effectiveness

Course Content

Welcome and AI management foundations
Audit the processes covered by welcome and ai management foundations.

Course route and lead auditor responsibilities
AI systems, models and responsible use
Read the standard: clauses, annexes and PDCA

Context, leadership and objectives
Audit the processes covered by context, leadership and objectives.

AI risks, treatment and applicability
Audit the processes covered by ai risks, treatment and applicability.

AI system impact assessment
Audit the processes covered by ai system impact assessment.

People, resources and information
Audit the processes covered by people, resources and information.

Data governance throughout the life cycle
Audit the processes covered by data governance throughout the life cycle.

Responsible design and evaluation
Audit the processes covered by responsible design and evaluation.

Deployment, operation and responsible use
Audit the processes covered by deployment, operation and responsible use.

Suppliers, users and incident response
Audit the processes covered by suppliers, users and incident response.

Plan the internal audit
Prepare a risk-based programme, audit plan and justified sample.

Conduct the audit and evaluate evidence
Interview, corroborate evidence, calculate measures and write supported findings.

Report findings and verify action
Report conclusions, evaluate corrective action and verify effectiveness.

Lead audit preparation and programme
Advanced leadership and practical application to AI risk, system impacts, data, evaluations, oversight and changed deployments.

Audit execution and team leadership
Advanced leadership and practical application to AI risk, system impacts, data, evaluations, oversight and changed deployments.

Findings communication and reporting
Advanced leadership and practical application to AI risk, system impacts, data, evaluations, oversight and changed deployments.

Corrective action follow up and assessment
Advanced leadership and practical application to AI risk, system impacts, data, evaluations, oversight and changed deployments.

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About Course

What Will You Learn?

Course Content

Welcome and AI management foundations Audit the processes covered by welcome and ai management foundations.

Course route and lead auditor responsibilities

AI systems, models and responsible use

Read the standard: clauses, annexes and PDCA

Context, leadership and objectives Audit the processes covered by context, leadership and objectives.

Context, interested parties and the AIMS scope

Leadership, policy, responsibilities and concerns

Objectives, opportunities and planned changes

AI risks, treatment and applicability Audit the processes covered by ai risks, treatment and applicability.

Recognize AI risks and opportunities

Assess AI risks consistently

Treat risks and prepare the Statement of Applicability

AI system impact assessment Audit the processes covered by ai system impact assessment.

Plan an AI system impact assessment

Consider individuals and groups

Consider society and communicate assessment findings

People, resources and information Audit the processes covered by people, resources and information.

Provide and document suitable resources

Build competence and practical awareness

Communicate and control documented information

Data governance throughout the life cycle Audit the processes covered by data governance throughout the life cycle.

Select and acquire suitable data

Evaluate data quality and representativeness

Maintain provenance and control data preparation

Responsible design and evaluation Audit the processes covered by responsible design and evaluation.

Turn responsible design into clear requirements

Distinguish verification and validation

Evaluate limitations, security and difficult cases

Deployment, operation and responsible use Audit the processes covered by deployment, operation and responsible use.

Deploy and change AI systems under control

Monitor operation, documentation and event logs

Use AI responsibly and maintain effective human oversight

Suppliers, users and incident response Audit the processes covered by suppliers, users and incident response.

Manage suppliers, customers and shared responsibilities

Inform users and provide reporting routes

Respond to incidents and retire systems responsibly

Plan the internal audit Prepare a risk-based programme, audit plan and justified sample.

Risk based audit programmes

Objectives scope criteria and planning

Sampling and audit working papers

Conduct the audit and evaluate evidence Interview, corroborate evidence, calculate measures and write supported findings.

Opening interviews and observation

Corroboration and performance measures

Supported findings and audit conclusions

Report findings and verify action Report conclusions, evaluate corrective action and verify effectiveness.

Closing meetings and management review

Correction cause analysis and action plans

Effectiveness follow up and assessment

Lead audit preparation and programme Advanced leadership and practical application to AI risk, system impacts, data, evaluations, oversight and changed deployments.

Commission and audit agreement

Programme priorities and resources

Team competence and impartiality

Readiness review and audit stages

ISO/IEC 42001:2023 Audit programme and engagement plan

Audit execution and team leadership Advanced leadership and practical application to AI risk, system impacts, data, evaluations, oversight and changed deployments.

Two day audit planning

Evidence strategy and sampling

Opening meeting and communication

Neutral interviewing and challenge

ISO/IEC 42001:2023 Evidence evaluation and defensible findings

Findings communication and reporting Advanced leadership and practical application to AI risk, system impacts, data, evaluations, oversight and changed deployments.

Remote evidence and information protection

Team reviews and changing priorities

Findings and classification

Closing meeting and difficult conversations

ISO/IEC 42001:2023 Observed audit leadership simulation

Corrective action follow up and assessment Advanced leadership and practical application to AI risk, system impacts, data, evaluations, oversight and changed deployments.

Reports and supported conclusions

Corrective action review

Follow up and continuing competence

Integrated lead audit rehearsal

ISO/IEC 42001:2023 Final report corrective action and follow up

ISO/IEC 42001:2023 Lead Auditor final knowledge examination

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