Module 1 – Introduction to ISO/IEC 42001 and Artificial Intelligence Management Systems (AIMS)

• Overview of ISO/IEC 42001:
  Understanding the purpose, scope, and structure of the ISO/IEC 42001 standard, designed to establish, implement, maintain, and continuously improve an Artificial Intelligence Management System (AIMS).
  Overview of the standard’s alignment with other ISO management system frameworks (e.g., ISO 9001, ISO/IEC 27001).
• Principles and Benefits of AI Governance:
  Introduction to responsible AI principles—transparency, fairness, accountability, and trustworthiness.
  How AI governance enhances compliance, mitigates ethical and operational risks, and supports sustainable innovation.
• Key Terminology and Concepts in AIMS:
  Definitions of essential terms such as AI system, model lifecycle, data governance, risk management, and human oversight.
  Understanding the difference between AI governance, AI ethics, and AI assurance.
• Structure and Requirements of the Standard:
  Clause-by-clause overview of ISO/IEC 42001: leadership, planning, support, operation, performance evaluation, and improvement.
  Integration with organizational strategies and regulatory frameworks.

Module 2 – Auditing Fundamentals

• Principles of Auditing (ISO 19011 and ISO/IEC 17021-1):
  Foundational auditing principles including integrity, objectivity, confidentiality, and evidence-based approach.
  Understanding how ISO 19011 guides auditing processes and ISO/IEC 17021-1 defines certification requirements.
• Auditor Responsibilities and Ethics:
  Role and conduct of the auditor in ensuring impartiality, independence, and professional competence.
  Managing conflicts of interest and upholding ethical standards during AI system assessments.
• Risk-Based Audit Approach:
  Applying risk-based thinking to prioritize audit activities according to AI impact, complexity, and risk exposure.
  Tailoring audit plans to address high-risk AI functions such as automated decision-making or machine learning models.
• Types of Audits: Internal, External, and Certification:
  Distinguishing between first-, second-, and third-party audits.
  Purpose, scope, and methodologies for each type, with emphasis on AI-specific audit considerations.

Module 3 – Planning and Preparing the Audit

• Understanding the AI Context and Scope:
  Assessing the organization’s AI environment, data ecosystem, and operational objectives.
  Identifying legal, ethical, and regulatory requirements relevant to AI.
• Defining Audit Objectives, Criteria, and Scope:
  Establishing clear audit goals, measurable criteria, and boundaries of assessment.
  Ensuring alignment with ISO/IEC 42001 requirements and organizational AI strategy.
• Developing Audit Checklists and Tools:
  Creating structured tools and templates for evidence gathering.
  Adapting checklists to assess data management, model governance, algorithmic transparency, and bias mitigation.
• Audit Team Selection and Assignments:
  Criteria for selecting competent auditors with both technical AI and management system expertise.
  Defining roles, responsibilities, and communication lines within the audit team.

Module 4 – Conducting the Audit

• Opening Meeting and Communication:
  Setting the stage for an effective audit through clear communication of objectives, methodology, and timelines.
  Building rapport with auditees and defining expectations.
• Evidence Collection Methods:
  Utilizing various techniques including interviews, document reviews, and observations.
  Evaluating AI documentation such as data lineage reports, algorithm validation records, and ethical risk assessments.
• Sampling Techniques in AI Environments:
  Understanding data sampling in complex AI systems.
  Ensuring representative and unbiased samples when evaluating model performance or compliance.
• Nonconformity Identification and Classification:
  Identifying deviations from ISO/IEC 42001 requirements.
  Categorizing nonconformities as major, minor, or opportunities for improvement, with AI-specific examples.

Module 5 – Reporting and Follow-Up

• Preparing Audit Reports:
  Structuring audit findings in a clear, concise, and evidence-based format.
  Highlighting strengths, weaknesses, and potential risks within the AIMS.
• Communicating Audit Findings:
  Techniques for presenting results effectively during closing meetings.
  Providing actionable recommendations and maintaining a professional, constructive tone.
• Corrective Actions and Continual Improvement:
  Understanding the process for addressing nonconformities and implementing corrective measures.
  Promoting a culture of continuous improvement within AI governance frameworks.
• Closing Meeting and Client Engagement:
  Summarizing key findings and next steps.
  Ensuring client understanding and commitment to follow-up actions.

Module 6 – Certification and Competence

• Auditor Certification Process:
  Overview of qualification pathways for ISO/IEC 42001 Lead Auditors.
  Examination and competency requirements for certification and registration.
• Maintaining Auditor Competence:
  Importance of continual professional development in AI auditing.
  Keeping up-to-date with evolving standards, emerging technologies, and regulatory expectations.
• Emerging Trends in AI Governance Audits:
  Discussion of evolving challenges in AI ethics, transparency, and accountability.
  Integrating new technologies such as generative AI and automated audit tools.

• Case Studies and Practical Exercises:
  Real-world audit simulations covering different AI domains (e.g., healthcare, finance, autonomous systems).
  Group activities for identifying nonconformities, writing reports, and presenting audit conclusions.