Introduction:

ISO/IEC 42001 establishes an international institutional standard that align artificial intelligence technologies with transparency, accountability, fairness, and risk oversight principles.It addresses governance structures that regulate the responsible development and management of artificial intelligence systems within organizations. This training program examines the structural components of an Artificial Intelligence Management System and the organizational context required for its governance. It also presents frameworks, system structures, and management processes used to organize, evaluate, and improve AI management systems aligned with ISO/IEC 42001 requirements.

Program Objectives

By the end of this program, participants will be able to:

• Identify the foundational principles and governance concepts of artificial intelligence management systems.

• Evaluate the structural requirements and components of an Artificial Intelligence Management System aligned with ISO/IEC 42001.

• Assess planning and risk governance frameworks related to AI system management.

• Examine operational structures and support mechanisms governing AI system oversight.

• Explore performance monitoring structures and improvement mechanisms within AI management systems.

Target Audience:

• AI governance professionals.

• IT managers and digital transformation specialists.

• Compliance and risk management professionals.

• Technology consultants and AI project advisors.

• Professionals responsible for organizational AI oversight.

Program Outline:

Unit 1:

Foundations of Artificial Intelligence Management Systems:

• Conceptual foundations of artificial intelligence governance frameworks.

• Institutional role of artificial intelligence management systems within organizations.

• Structural principles underlying ISO/IEC 42001 governance architecture.

• Organizational context analysis within AI governance frameworks.

• Terminology structures and conceptual models used in AI management systems.

Unit 2:

Structure of the Artificial Intelligence Management System:

• Artificial intelligence management system architecture and structural components.

• Policy frameworks and leadership governance within AI management structures.

• Organizational roles, responsibilities, and authority structures in AIMS governance.

• Documentation structures supporting AI management system organization.

• Integration models connecting AI governance with existing management systems.

Unit 3:

Planning and Risk Governance in Artificial Intelligence Systems:

• Risk identification frameworks related to artificial intelligence systems.

• Risk evaluation structures within AI lifecycle governance models.

• Planning frameworks supporting AI management system alignment with organizational strategy.

• Ethical governance principles related to transparency, fairness, and accountability in AI systems.

• Control frameworks addressing AI risks, data integrity, and model reliability.

Unit 4:

Operational and Support Structures of AIMS:

• Resource management frameworks supporting artificial intelligence governance structures.

• Competence, awareness, and knowledge management systems within AI governance environments.

• Operational governance models for AI system lifecycle oversight.

• Communication and information management structures supporting AIMS operation.

• Control structures governing AI development, deployment, and operational monitoring.

Unit 5:

Performance Evaluation and Improvement of AI Management Systems:

• Monitoring and measurement frameworks used to assess AI governance effectiveness.

• Internal audit structures within artificial intelligence management systems.

• Management review frameworks supporting strategic oversight of AIMS performance.

• Corrective action and improvement structures within AI governance programs.

• Continual improvement models supporting long-term maturity of artificial intelligence management systems.