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Artificial intelligence is reshaping business processes and decision-making, creating new assurance challenges across data, models, system interactions and third-party services. This course equips participants to evaluate AI governance, risk, operations and controls, and to apply audit approaches suited to AI-enabled environments. They will work through modules on AI governance and risk, AI operations, and AI auditing tools and techniques, including model evaluation, lifecycle controls, audit testing, evidence collection, data analytics and reporting.
Learning Outcomes
- Analyse the organisational impact, opportunities and risks associated with AI solutions.
- Evaluate AI governance, policies, accountability structures and compliance with legal and regulatory requirements.
- Assess AI models, algorithms, data inputs and lifecycle processes for alignment, reliability, fairness and compliance.
- Evaluate the design and effectiveness of AI controls, including privacy, access, change management and incident response controls.
- Apply audit planning, testing, sampling, evidence collection and data analytics techniques to AI-enabled environments.
- Evaluate and use AI solutions to improve audit planning, execution and reporting.
Key Topics
- AI models, governance, programme management, risk management, privacy and data governance.
- AI ethics, regulations, standards, organisational accountability and responsible AI practices.
- AI data management, solution development methodologies and lifecycle controls.
- Supervision of AI outputs, impacts and decisions, model testing and change management.
- AI threats, vulnerabilities, incident response, vendors and supply-chain management.
- AI audit planning, testing, evidence collection, data analytics, reporting and certification preparation for the ISACA Advanced in AI Audit (AAIA) exam.
Exam Details
This course is designed to build participants’ understanding of key concepts and domains covered in the ISACA Advanced in AI Audit (AAIA) 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 Governance and Risk
- AI models, considerations and requirements
- AI governance and programme management
- AI risk management
- Privacy and data governance programmes
- Leading practices, ethics, regulations and standards for AI
Domain 2: AI Operations
- Data management specific to AI
- AI solution development methodologies and lifecycle
- Change management specific to AI
- Supervision of AI outputs, impacts and decisions
- Testing techniques for AI solutions
- AI-specific threats and vulnerabilities
- AI-specific incident response management
Domain 3: AI Auditing Tools and Techniques
- Audit planning and design
- Audit testing and sampling methodologies
- Audit evidence collection techniques
- Audit data quality and data analytics
- AI audit outputs and reports
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