Reference Objectives and Controls for Artificial Intelligence
Description: After completing the course, participants will be able to demonstrate the following competences:
- Identify and interpret AI reference objectives and control families.
- Select, tailor, and implement appropriate controls for specific AI use cases.
- Document the justification and applicability of AI controls.
- Assess control effectiveness and alignment with AI risk profiles.
- Support organizational compliance, accountability, and AI governance processes.
Previous skills/knowledge: Participants are expected to have the following basic knowledge:
- Basic understanding of artificial intelligence systems and associated risks.
- Familiarity with management system standards and control-based approaches.
- General awareness of ethical, legal, and organizational issues related to AI deployment.
Authorized Partners:
Teaching requirements: Trainers should meet the following requirements:
- Subject Matter Expertise: deep understanding of AI risk management, control frameworks, and alignment with ISO/IEC 42001, ISO/IEC 23894, and ISO/IEC 27002.
- Certifications: recommended certifications include ISO/IEC 27001 Lead Implementer, ISO/IEC 42001 qualifications, or equivalent AI governance and ethics credentials.
- Training & Practical Experience: at least 2–3 years of practical experience in implementing, evaluating, or designing AI-specific controls and mitigation strategies, ideally in regulated or high-impact sectors.
Objectives to achieve: The course aims to achieve the following objectives:
- Understand the role of reference objectives and controls in AI governance.
- Familiarize participants with the structure and use of Annex A in ISO/IEC 42001 and the control families it defines.
- Develop the ability to assess and implement AI-specific controls based on identified risks and system objectives.
- Support the alignment of AI control frameworks with legal, ethical, and performance requirements.
- Enable organizations to select and document applicable controls in support of transparency, robustness, and trustworthiness.
