This foundation course introduces ISO/IEC 42001:2023 and the core concepts of an Artificial Intelligence Management System (AIMS). Participants gain an understanding of how organizations establish, implement, maintain, and continually improve an AIMS to support responsible development, provision, and use of AI. The course covers governance, organizational responsibilities, AI risks and impacts, operational controls, and continual improvement.

Duration 2 Days – 14 hrs.

Objectives

• Explain the purpose, scope, and benefits of ISO/IEC 42001.
• Describe the fundamental concepts and terminology of an AIMS.
• Identify the main requirements of ISO/IEC 42001 and their organizational relevance.
• Explain leadership responsibilities, AI policies, and governance roles.
• Describe the relationship between AI risk management and AI system impact evaluation.
• Recognize the purpose of Annex A controls and Annex B implementation guidance.
• Identify essential documentation and operational practices that support an AIMS.
• Explain how performance evaluation and continual improvement support effective AI management.

 

Target Audience 

• Managers and team leaders involved in AI adoption or governance.
• Governance, risk, and compliance professionals.
• Information security, privacy, and quality management personnel.
• AI, data science, IT, and software development professionals.
• Business process owners and product managers responsible for AI-enabled services.
• Internal auditors seeking introductory knowledge of AI management systems.
• Professionals beginning their learning journey in ISO/IEC 42001.

  

Prerequisites 

• No prior ISO/IEC 42001 training is required.
• Basic awareness of AI concepts and common business applications is helpful.
• Familiarity with organizational processes and responsibilities is beneficial.
• Prior knowledge of ISO management systems is useful but not required.
• Programming or machine learning development experience is not required.

 

 

Course Outline 

Day 1: AI Management System Foundations and Requirements

Module 1: Introduction to AI and Responsible AI Management

• Fundamental AI concepts and terminology.
• Common organizational uses of AI.
• Benefits, limitations, and challenges of AI systems.
• Fairness, transparency, accountability, privacy, and human oversight.
• The purpose of structured AI governance.

 Module 2: Introduction to ISO/IEC 42001

• Purpose, scope, and intended application of the standard.
• Key concepts of an Artificial Intelligence Management System.
• Structure of ISO/IEC 42001 and its annexes.
• Overview of Clauses 4–10.
• Relationship with other management systems and AI-related standards.

 Module 3: Organizational Context and Leadership

• Internal and external issues relevant to AI management.
• Interested parties and their requirements.
• Defining the scope of the AIMS.
• Leadership commitment and AI policy.
• Organizational roles, responsibilities, and authorities.

 Module 4: Planning and Support

• Risks and opportunities affecting the AIMS.
• Introduction to AI risk identification, analysis, evaluation, and treatment.
• AI system impacts on individuals, groups, and society.
• AI objectives and planning to achieve them.
• Resources, competence, awareness, and communication.
• Documented information and its control.

  

Day 2: Controls, Operations, and Continual Improvement

Module 5: AI Controls and Supporting Guidance

• Purpose and structure of Annex A.
• Relationship between Annex A controls and Annex B guidance.
• Selecting controls and understanding the Statement of Applicability.
• Policies, internal organization, and resources for AI.
• AI system impacts and life cycle controls.
• Data management and information for interested parties.
• Responsible AI use and third-party relationships.

 Module 6: Operational Management of AI Systems

• Operational planning and control.
• Applying AI risk and impact processes during operations.
• Managing AI system development, deployment, use, and changes.
• Data quality, provenance, and suitability.
• Supplier and externally provided AI service considerations.
• Maintaining operational documentation and evidence.

 Module 7: Performance Evaluation and Improvement

• Monitoring, measurement, analysis, and evaluation.
• Purpose and scope of internal audits.
• Management review and leadership oversight.
• Nonconformities and corrective actions.
• Continual improvement of the AIMS.

 Module 8: Applying Foundation Knowledge to an Organizational Scenario

• Identify an AI use case and its relevant stakeholders.
• Outline an appropriate AIMS scope.
• Recognize key AI risks and potential impacts.
• Connect relevant controls to the identified risks.
• Identify governance responsibilities and supporting documentation.
• Summarize initial priorities for responsible AI management.