ISO/IEC 42001:2023 Training Workshop– Artificial Intelligence Management Systems (AIMS) by Tonex
ISO/IEC 42001:2023 – Artificial Intelligence Management Systems (AIMS) is the world’s first international standard specifically designed to help organizations manage the development and use of artificial intelligence responsibly, ethically, and effectively. This 2-day intensive training course provides participants with a deep understanding of the ISO/IEC 42001:2023 standard, its structure, requirements, and practical guidance for implementation.
Through expert-led instruction, interactive workshops, real-world case studies, and practical exercises, participants will explore how to design, implement, and continuously improve an Artificial Intelligence Management System (AIMS) aligned with ISO/IEC 42001. The course highlights the importance of addressing unique AI-specific risks such as algorithmic bias, lack of transparency, explainability challenges, and ethical concerns.
Participants will also learn how to integrate AIMS with existing management systems (e.g., ISO 27001, ISO 9001), navigate the AI lifecycle from development to monitoring, and prepare for ISO/IEC 42001 certification. Whether your organization builds AI systems or deploys them from third parties, this course equips teams to ensure compliance, trustworthiness, and long-term sustainability of AI technologies.
Learning Objectives
By the end of this course, participants will be able to:
- Understand the Scope and Purpose of ISO/IEC 42001:2023
- Explain the structure, intent, and applicability of the standard.
- Describe how it supports responsible AI governance and risk management.
- Identify Organizational Requirements for Establishing an AI Management System (AIMS)
- Analyze internal and external issues relevant to AI adoption.
- Define the scope of an AIMS in line with strategic objectives and stakeholder needs.
- Implement Governance, Accountability, and Leadership Roles for AI
- Develop AI governance structures that ensure ethical and transparent practices.
- Assign clear responsibilities and authorities across the AI system lifecycle.
- Integrate Risk-Based Thinking and Ethical Principles into AI Planning
- Apply risk assessment and mitigation strategies tailored to AI systems.
- Address potential harms, misuse, and ethical concerns proactively.
- Manage AI Operations Effectively Across the Lifecycle
- Establish operational controls from data acquisition through model deployment and monitoring.
- Ensure traceability, transparency, explainability, and documentation of AI systems.
- Ensure Competency, Awareness, and Communication in AI Projects
- Build organizational capacity and awareness for responsible AI development and use.
- Promote transparency and effective communication across teams and stakeholders.
- Monitor, Audit, and Review AI Management Systems
- Set up performance evaluation mechanisms for AIMS effectiveness.
- Plan and conduct AI-specific internal audits and management reviews.
- Drive Continuous Improvement in AI Systems and Practices
- Identify nonconformities and implement corrective actions.
- Develop mechanisms to adapt AIMS to evolving risks, regulations, and technologies.
- Prepare for ISO/IEC 42001 Certification
- Map implementation requirements to the standard’s clauses.
- Understand documentation, readiness activities, and certification pathways.
Target Audience:
- AI Governance Officers
- Compliance Managers
- Risk Managers
- Data Scientists/Engineers working on AI
- Quality Assurance Professionals
- Senior Management in AI-centric companies
- Consultants and Auditors
Prerequisites:
- Basic knowledge of AI concepts
- Familiarity with management system standards (e.g., ISO/IEC 27001, ISO 9001) is helpful
Day 1: Foundations and Core Requirements of ISO/IEC 42001
Module 1: Introduction to ISO/IEC 42001:2023
- Background of the standard
- Scope and applicability
- Relation to other standards (e.g., ISO/IEC 27001, ISO/IEC 38507)
- Key AI-specific challenges addressed by ISO/IEC 42001
Activities:
- Case studies: AI-related failures & need for structured management
- Interactive discussion: Why AIMS matters
Module 2: Context of the Organization
- Understanding internal and external issues
- Understanding stakeholder needs and expectations
- Defining the scope of AIMS
- Integration with strategic objectives
Workshop:
- Stakeholder mapping and organizational AI context analysis
Module 3: Leadership and Governance for AI Systems
- Leadership commitment and AI accountability
- Establishing AI governance roles and responsibilities
- AI policy requirements
- Ethical AI principles (e.g., fairness, transparency, explainability)
Exercise:
- Drafting a sample AI policy aligned with ISO/IEC 42001
Module 4: Planning the AI Management System
- Risk-based thinking in AI
- Objectives for the AIMS and planning to achieve them
- Addressing AI system lifecycle risks
- AI system purpose, limitations, and potential impacts
Lab:
- AI system risk assessment framework template walkthrough
Day 2: Implementation, Evaluation, and Improvement
Module 5: Support & Operational Controls
- Competency and training for AIMS
- AI data governance and data quality controls
- Documentation and operational planning
- Managing third-party AI systems and outsourcing
Exercise:
- Role-play: Managing third-party AI vendor compliance
Module 6: Operation of AI Systems under AIMS
- Lifecycle of AI systems: Design, development, deployment, monitoring
- Transparency, traceability, explainability measures
- Record-keeping and documentation control
- Specific controls for high-risk AI systems
Workshop:
- Mapping AI system lifecycle to ISO/IEC 42001 clauses
Module 7: Performance Evaluation
- Monitoring, measurement, analysis, and evaluation
- AI-specific audits and assessment strategies
- Management reviews focused on AI system performance
Exercise:
- Conducting a mock internal audit of an AI management system
Module 8: Improvement and Continual Optimization
- Addressing nonconformities and taking corrective actions
- Continual improvement mechanisms for AIMS
- Integration with continual learning for AI models
- Adapting AIMS to evolving AI regulations and standards
Lab:
- Create a continual improvement roadmap for an AI team
Wrap-Up and Certification
- Summary of key takeaways
- Sample certification roadmap (e.g., ISO 42001 implementation phases)
- Final Q&A session
- Course evaluation
Optional:
- Short exam or quiz
- Certificate of Completion