AAIA (ISACA Advanced in AI Audit™)

Domain 1 – AI Governance and Risk

AI Models, Considerations, and Requirements

• Types of AI:
  • Generative AI
  • Predictive AI
  • Narrow AI
  • General AI
• Machine learning and neural network fundamentals
• AI algorithms and technical concepts
• AI lifecycle management:
  • Planning and design
  • Data collection and processing
  • Model development and deployment
  • Monitoring and decommissioning

AI Governance and Program Management

• AI strategy and governance alignment
• AI-related roles and responsibilities
• AI governance policies and procedures
• Skills and competencies for AI oversight
• Program metrics and governance indicators

AI Risk Management

• AI threat landscape
• AI-specific risks and vulnerabilities
• Risk assessments and mitigation strategies
• Risk appetite and prioritization
• Continuous monitoring and performance metrics

Privacy, Data Governance, Ethics, and Regulation

• Data governance and classification
• Data retention and licensing
• Privacy and consent management
• Ethical AI considerations:
  • Bias and fairness
  • Transparency and explainability
  • Trust and safety
  • Human rights and IP considerations
• AI regulations, standards, and frameworks

Domain 2 – AI Operations

AI Data Management and Solution Lifecycle

• Data collection and quality management
• Data confidentiality and security
• AI solution development lifecycle
• Privacy and security by design
• Explainability and robustness

AI Change Management and Operational Supervision

• AI-specific change management considerations
• AI observability and monitoring
• Human-in-the-loop (HITL) governance
• AI hallucination management
• AI operational readiness and sustainability

AI Testing, Threats, and Incident Management

• Conventional and AI-specific testing methodologies
• Bias and adversarial testing
• AI-related threats:
  • Data poisoning
  • Prompt injection
  • Model theft and inversion
  • Vendor AI risks
• AI incident response and recovery processes

Domain 3 – AI Auditing Tools and Techniques

AI Audit Planning and Design

• AI asset and control identification
• AI audit scoping and objectives
• AI governance control reviews
• AI use cases for audit enhancement
• Generative AI in audit processes

AI Audit Testing and Sampling

• AI audit methodologies
• AI testing approaches
• AI-assisted sampling techniques
• Audit optimization and anomaly detection
• AI audit outcome analysis

Audit Evidence Collection and Reporting

• AI-enhanced data collection techniques
• Structured and unstructured data analysis
• AI-assisted interviews and walkthroughs
• AI audit analytics and dashboards
• Reporting, visualizations, and automated follow-up

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