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AI150: AIMS Auditor/Lead Auditor ISO/IEC 42001:2023 NEW

Training: Artificial Intelligence - Audit

The AIMS Auditor/Lead Auditor ISO/IEC 42001:2023 course provides hands-on knowledge for auditing AI management systems. Participants learn how to plan and perform audits, lead audit teams and apply AI-specific audit procedures. Practice-oriented case studies and simulations specifically prepare participants for the Lead Auditor certification.

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Possible course languages: German

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Agenda:

Technical fundamentals & AI management system
- Introduction to AI systems
- Types of AI systems and their applications
- Basic AI architecture and mode of operation
- Current developments and trends
- Purpose, structure and scope of ISO/IEC 42001
- Legal and regulatory requirements (e.g. GDPR, AI Act, Data (Governance) Act) in the AI context
Audit fundamentals & auditor competence
- Audit principles according to ISO 19011
- Standards for lead auditors
- Interaction of ISO/IEC 42001 and ISO/IEC 42006 (ISO 17021)
Audit planning & preparation
- The certification process according to ISO 17021
- Structure of an audit programme for AI management systems
- Risk-based audit planning (objectives, scope, criteria)
- Integration of AI assessment aspects into audit planning
- Preparation of audit checklists and document reviews
- Roles, responsibilities and professional audit ethics
- Leading an audit team: role allocation, delegation, time and resource management, communication
Audit execution & interview strategies
- Execution of Stage 2 audits (on-site / remote)
- Interview strategies in AI audits: open, in-depth and targeted questioning techniques
- Dealing with subject matter experts and management as well as with resistance
- Evidence collection related to AI: training data, model validation, bias detection
- Evaluation of AI-specific risks and nonconformities
AI-technical test methods & audit practices
- Ethical assessment of AI systems
- Technical evaluation: training data, data pipelines, model performance, bias detection, fairness, security and robustness
- Quality assurance techniques and audit tests
- Integration of specific test procedures: VDE SPEC 90012, TÜV AI.AM Assessment Matrix
Audit closure, reporting & follow-up
- Evaluation and consolidation of audit findings
- Dealing with difficult audit situations
- Closing meetings with audited functions
- Preparation of meaningful audit reports
- Handling of corrective and improvement measures
- Ensuring traceability and audit trails in AI audits
Practice, case studies & examination
- Audit simulations including team leadership and interview exercises
- Case studies from real AI audit contexts
- Review of key audit principles and AI specifics
- Qualifying lead auditor examination (including AI audit elements)
Didactic focus
- Practice-oriented audit role plays and realistic scenarios
- Interactive group work, reflection and peer feedback
- Integration of technical AI assessment aspects into classical audit methodology
- Teaching soft SAIlls for team leadership and communication

Objectives:

The AIMS Auditor/Lead Auditor ISO/IEC 42001:2023 course enables participants to,

gain an in-depth understanding of the requirements and structure of an AI Management System (AIMS) in accordance with ISO/IEC 42001.
gain an in-depth understanding of the certification process according to ISO 17021 in combination with ISO/IEC 42006.
translate the relevant legal frameworks and standards (e.g. EU AI Act, GDPR) into audit contexts.
plan audit programmes on a risk-based basis, including audit objectives, scope and criteria.
prepare and conduct audits in a structured manner, with a particular focus on AI-related requirements and risks.
lead audit teams in a professional manner and apply audit methods, including effective interview and questioning techniques.
assess technical and qualitative aspects of AI systems (e.g. data, model validation, bias, security) in the audit.
correctly identify, document and follow up nonconformities and audit findings.
prepare audit reports in a clear and transparent manner and professionally facilitate closing meetings.
acquire practice-oriented knowledge of audit execution, the application of relevant test procedures and quality assurance measures, combined with the ability to apply ethical, security and regulatory requirements in their own audit contexts.

Target audience:

The AIMS Auditor/Lead Auditor ISO/IEC 42001:2023 course is aimed at all individuals who

want to introduce, operate or improve a formal AI Management System (AIMS) in accordance with ISO/IEC 42001
as auditors wish to deepen their knowledge and skills in auditing management systems with a specific focus on artificial intelligence
as consultants or specialists want to advise companies on ISO/IEC 42001 certifications and AI audit processes
work in the areas of quality, risk or compliance management and wish to expand their competencies to include AI audit methods
are required to assess and evaluate technical, organizational or regulatory aspects of AI systems as part of audits

Prerequisites:

Prior attendance of the AI120 AIMS Implementation according to ISO/IEC 42001:2023 or experience with management systems as well as prior basic engagement with ISO/IEC 42001:2023
Prior attendance of the SC135 Internal Auditor or comparable audit knowledge

Description:

The AIMS Auditor/Lead Auditor ISO/IEC 42001:2023 course is aimed at individuals who, in their roles, are responsible for or significantly support the assessment, auditing and certification of Artificial Intelligence Management Systems (AIMS). This includes in particular:

Auditors who wish to assume lead auditor responsibility in AI-related management system audits
Professionals and managers from the areas of quality, compliance, risk and security management who design
or support AI audit processes
Persons responsible for the implementation, operation or further development of an AIMS in accordance with ISO/IEC 42001
Consultants who advise companies on audit and certification projects related to AI management
Specialists who wish to deepen their knowledge of AI audit methods and AI-specific assessment requirements