ISACAISACA Advanced in AI Audit
The ISACA Advanced in AI Audit (AAIA) certification is the world's first advanced AI audit credential, designed for experienced IT auditors and advisors who hold CISA or another qualified designation. It validates your ability to assess AI risks, ensure compliance, and leverage AI to meet strategic goals. Earning AAIA positions you as a leader in the emerging AI-driven audit landscape.
612 practice questions · Updated 2026-07-30
3Domains
17Objectives
167Concepts
612Questions
AAIA Curriculum
Every domain, objective, and concept the AAIA exam measures.
- AI Model Lifecycle
- Model Risk Management
- Model Validation and Testing
- Model Governance
- Regulatory and Compliance Requirements
- Ethical Considerations in AI Models
- Data Requirements for AI Models
- Model Interpretability and Explainability
- Model Performance Metrics
- AI Model Auditing
- AI Governance Frameworks
- AI Governance Roles and Responsibilities
- AI Governance Structures
- AI Strategy Alignment
- AI Policy and Standards Development
- AI Risk Management Integration
- AI Ethics and Responsible AI
- AI Regulatory and Compliance Considerations
- AI Program Management
- AI Performance Monitoring and Reporting
- AI Change Management
- AI Assurance and Audit Readiness
- Risk Identification
- Risk Assessment
- Risk Response
- Risk Monitoring and Reporting
- AI Risk Governance
- Integration with Enterprise Risk Management
- Privacy Program Frameworks
- Data Governance Principles
- Privacy by Design and Default
- Data Classification and Inventory
- Consent and Lawful Basis Management
- Data Subject Rights Handling
- Privacy Impact Assessment (PIA)
- Data Retention and Deletion Policies
- Cross-Border Data Transfer Mechanisms
- Privacy Metrics and Monitoring
- Third-Party and Vendor Privacy Risk
- Incident Response and Breach Notification
- Regulatory Compliance Mapping
- Privacy Culture and Training
- AI Governance Frameworks
- AI Ethics Principles
- AI Regulations and Compliance
- AI Risk Management Standards
- Ethical AI Implementation
- Data Lifecycle Management in AI
- Data Quality for AI
- Data Lineage and Provenance
- Data Privacy and Protection
- Data Security Controls
- Data Governance Frameworks
- Data Bias and Fairness
- Regulatory and Compliance Requirements
- Data Storage and Architecture
- Data Integration and Preparation
- Data Retention and Disposal
- Data Auditing and Monitoring
- AI Solution Development Methodologies
- AI Lifecycle Phases
- Requirements and Feasibility Analysis
- Data Management and Preparation
- Model Development and Validation
- Deployment and Integration
- Monitoring and Maintenance
- Governance and Compliance
- Stakeholder Communication and Change Management
- AI Change Management Fundamentals
- AI Model Lifecycle Changes
- Impact Assessment of AI Changes
- Version Control for AI Assets
- AI Model Retraining and Updates
- Change Approval and Governance
- Testing and Validation of AI Changes
- Rollback and Contingency Planning
- Communication and Training for AI Changes
- Monitoring and Post-Change Review
- Supervision of AI Outputs
- Impact Assessment of AI Solutions
- Decision Oversight in AI
- Monitoring and Feedback Mechanisms
- Handling AI Output Errors
- Governance of AI Supervision
- Documentation and Reporting of AI Supervision
- Testing Techniques Overview
- Test Data Preparation
- Functional Testing
- Performance Testing
- Robustness Testing
- Security Testing
- Bias and Fairness Testing
- Explainability and Interpretability Testing
- Model Validation and Verification
- Integration Testing
- User Acceptance Testing
- Regression Testing
- Monitoring and Continuous Testing
- Documentation and Reporting of Test Results
- AI-Specific Threat Landscape
- Adversarial Attacks
- Data Poisoning
- Model Inversion and Extraction
- Prompt Injection
- AI Supply Chain Vulnerabilities
- AI-Specific Vulnerabilities in Deployment
- Mitigation Strategies for AI Threats
- AI Incident Definition and Classification
- AI Incident Response Lifecycle
- AI Incident Detection and Monitoring
- AI Incident Triage and Prioritization
- AI Incident Containment and Mitigation
- AI Incident Eradication and Recovery
- AI Incident Communication and Escalation
- AI Incident Documentation and Forensics
- AI Incident Post-Mortem and Lessons Learned
- AI-Specific Incident Response Challenges
- AI Incident Response Integration with IT and Security Operations
- Regulatory and Compliance Considerations in AI Incident Response
- Audit Planning Fundamentals
- Risk-Based Audit Approach
- Understanding the AI System Under Audit
- Defining Audit Objectives and Scope
- Selecting Audit Criteria and Standards
- Designing Audit Procedures and Tests
- Resource Planning and Team Composition
- Stakeholder Engagement and Communication
- Audit Timeline and Milestones
- Documentation and Workpaper Standards
- Ethical and Independence Considerations
- Continuous Monitoring and Adaptation
- Sampling Fundamentals
- Sampling Risk and Non-sampling Risk
- Statistical vs. Non-statistical Sampling
- Sample Selection Methods
- Determining Sample Size
- Evaluating Sample Results
- Attribute Sampling
- Variables Sampling
- Monetary Unit Sampling (MUS)
- Stratification
- Sampling in AI Auditing
- Use of Audit Tools for Sampling
- Documentation of Sampling Procedures
- Evidence Collection Planning
- Sampling Techniques
- Automated Evidence Gathering
- Manual Evidence Collection
- Evidence Integrity and Chain of Custody
- Data Extraction and Transformation
- Documentation and Retention
- Data Quality Dimensions
- Data Quality Assessment Methods
- Data Quality Remediation
- Data Analytics Techniques
- Data Analytics Tools
- Data Analytics Process
- Data Visualization for Audit Reporting
- Data Analytics in Audit Testing
- Data Governance and Ethics in Analytics
- Documentation and Evidence Management
- AI Audit Report Structure
- Tailoring Reports to Stakeholders
- Communicating AI Findings
- Recommendations and Action Plans
- Documenting Evidence and Workpapers
- Report Quality and Review
Ready to practice?Test your knowledge with exam-style questions or take an intelligent quiz tailored to your level.
Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for AAIA, so none is invented.