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Artificial intelligence introduces risk across organisational strategy, governance, models, data, operations, regulatory obligations and third-party relationships. This course equips participants to integrate AI risk into enterprise governance and risk management, assess risks throughout the AI lifecycle, and implement effective treatment, control, monitoring and reporting practices. They will work through modules on AI risk governance and framework integration, AI lifecycle risk management, and AI risk programme management, including supply-chain risk, incident response and operational resilience.
Learning Outcomes
- Analyse risks associated with AI models and data, including design weaknesses, algorithmic bias, data integrity and model drift.
- Integrate AI risk considerations into enterprise governance, risk management frameworks and organisational processes.
- Develop AI risk governance structures, roles, accountability arrangements, policies and risk tolerance levels.
- Conduct AI-specific risk assessments, classify identified risks and recommend appropriate treatment strategies.
- Evaluate legal, regulatory, ethical and trustworthiness considerations, including transparency, safety, fairness and accountability.
- Assess AI controls, risk metrics, monitoring, reporting, supply-chain risk and operational resilience arrangements.
Key Topics
- AI models, frameworks, strategies, organisational use cases and enterprise alignment.
- AI ownership, oversight, accountability, policies, procedures and organisational training.
- AI regulatory compliance, legal considerations, trustworthiness, ethics and societal implications.
- AI design, development, procurement, documentation, model training, testing and validation.
- AI implementation, maintenance, decommissioning, data management and asset management.
- AI risk assessment, treatment, controls, metrics, monitoring, reporting, supply-chain risk, incident response and certification preparation for the ISACA Advanced in AI Risk (AAIR) exam.
Exam Details
This course is designed to build participants’ understanding of key concepts and domains covered in the ISACA Advanced in AI Risk (AAIR) certification.
To maximise success, participants are strongly encouraged to complement the course with additional self-study, revision of course materials, and dedicated practice before attempting the exam.
Domain 1: AI Risk Governance and Framework Integration
- AI models, frameworks, strategies and use cases
- AI organisational processes and alignment
- AI ownership, oversight and accountability
- AI policies, procedures and organisational training
- AI regulatory compliance and legal considerations
- AI trustworthiness, ethics and societal implications
Domain 2: AI Life Cycle Risk Management
- AI design, development, procurement and documentation
- AI model training, testing and validation
- AI implementation, maintenance and decommissioning
- AI data and asset management
Domain 3: AI Risk Programme Management
- AI risk scenario identification and assessment
- AI risk treatment strategies
- AI controls management
- AI risk metrics, monitoring and reporting
- AI supply-chain risk management
- AI incident response, business impact analysis, business continuity and disaster recovery
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