33% DOMAIN 1 – AI GOVERNANCE AND RISK
This Domain demonstrates your ability to advise stakeholders on implementing AI solutions through appropriate and effective policy, risk controls, data governance and ethical standards.
A–AI MODELS, CONSIDERATIONS, AND REQUIREMENTS
1. Types of AI 
2. Machine Learning/AI Models 
3. Algorithms 
4. AI Life Cycle
5. Business Considerations 
B–AI GOVERNANCE AND PROGRAM MANAGEMENT
1. AI Strategy 
2. AI-Related Roles and Responsibilities 
3. AI-Related Policies and Procedures  
4. AI Training and Awareness 
5. Program Metrics 
C –AI RISK MANAGEMENT 
1. AI-Related Risk Identification
2. Risk Assessment 
3. Risk Monitoring 
D–PRIVACY AND DATA GOVERNANCE PROGRAMS  
1. Data Governance
2. Privacy Considerations 
E–LEADING PRACTICES, ETHICS, REGULATIONS, AND STANDARDS FOR AI   
1. Standards, Frameworks, and Regulations Related to AI 
2. Ethical Considerations 

46% DOMAIN 2 – AI OPERATIONS
This domain confirms your skill in balancing sustainability, operational readiness, and the risk profile with the benefits and innovation AI promises to support enterprise-wide adoption of this powerful technology.
A–DATA MANAGEMENT SPECIFIC TO AI 
1. Data Collection 
2. Data Classification 
3. Data Confidentiality 
4. Data Quality
5. Data Balancing 
6. Data Scarcity
7. Data Security
B–AI SOLUTION DEVELOPMENT METHODOLOGIES AND LIFECYCLE 
1. AI Solution Development Life Cycle 
2. Privacy and Security by Design 
C–CHANGE MANAGEMENT SPECIFIC TO AI  
1. Change Management Considerations 
D–SUPERVISION OF AI SOLUTIONS   
1. AI Agency  
E–TESTING TECHNIQUES FOR AI SOLUTIONS     
1. Conventional Software Testing Techniques Applied to AI Solutions 
2. AI-Specific Testing Techniques  
F–THREATS AND VULNERABILITIES SPECIFIC TO AI      
1. Types of AI-Related Threats  
2. Controls for AI-Related Threats   
G–INCIDENT RESPONSE MANAGEMENT SPECIFIC TO AI       
1. Prepare   
2. Identify and Report 
3. Assess  
4. Respond   
5. Post-Incident Review

21% DOMAIN 3 – AI AUDITING TOOLS AND TECHNIQUES
This domain focuses on optimizing audit outcomes through innovation and highlights your knowledge of audit techniques tailored to AI systems and the use of AI-enabled tools streamline audit efficiency and provide faster, quality insight.
A–AUDIT PLANNING AND DESIGN
1. Identification of AI Assets 
2. Types of AI Controls 
3. AI Audit Use Cases 
4. Internal Training for AI Use 
B–AUDIT TESTING AND SAMPLING METHODOLOGIES  
1. Designing an AI Audit 
2. AI Audit Testing Methodologies 
3. AI Sampling
4. Testing AI Outcomes
5. Sample AI Audit Process 
C–AUDIT EVIDENCE COLLECTION TECHNIQUES 
1. Data Collection 
2. Walkthroughs and Interviews 
3. AI Collection Tools 
D–AUDIT DATA QUALITY AND DATA ANALYTICS 
1. Data Quality 
2. Data Analytics 
3. Data Reporting 
E–AI AUDIT OUTPUTS AND REPORTS 
1. Reports 
2. Audit Follow-up 
3. Quality Assurance 

SECONDARY CLASSIFICATIONS – TASKS
1. Evaluate impacts, opportunities, and risk when integrating AI solutions within the audit process.
2. Utilize AI solutions to enhance audit processes, including planning, execution, and reporting.
3. Evaluate AI solutions to advise on impact, opportunities, and risk to organization.
4. Evaluate the impact of AI solutions on system interactions, environment, and humans.
5. Evaluate the role and impact of AI decision-making systems on the organization and stakeholders.
6. Evaluate the organization’s AI policies and procedures, including compliance with legal and regulatory requirements.
7. Evaluate the monitoring and reporting of metrics (e.g., KPIs, KRIs) specific to AI.
8. Evaluate whether the organization has defined ownership of AI-related risk, controls, procedures, decisions, and standards.
9. Evaluate the organization’s data governance program specific to AI.
10. Evaluate the organization’s privacy program specific to AI.
11. Evaluate the organization’s problem and incident management programs specific to AI.
12. Evaluate the organization’s change management program specific to AI.
13. Evaluate the organization’s configuration management program specific to AI.
14. Evaluate the organization’s threat and vulnerability management programs specific to AI.
15. Evaluate the organization’s identity and access management program specific to AI.
16. Evaluate vendors and supply chain management programs specific to AI solutions.
17. Evaluate the design and effectiveness of controls specific to AI.
18. Evaluate data input requirements for AI models (e.g., data appropriateness, bias, privacy).
19. Evaluate system/business requirements for AI solutions to ensure alignment with enterprise architecture.
20. Evaluate the AI solution lifecycle (e.g., design, development, deployment, monitoring, and decommissioning) and inputs/outputs for compliance and risk.
21. Evaluate algorithms and models to ensure AI solutions are aligned to business objectives, policies, and procedures.
22. Analyze the impact of AI on the workforce to advise stakeholders on how to address AI-related workforce impacts, training, and education.
23. Evaluate that awareness programs align to the organization’s AI-related policies and procedures.