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Updated: Sep 12, 2026

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ISACA AAIR Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Risk (AAIR)
Exam Number:AAIR
Exam Duration:120 minutes
Certificate Validity Period:No fixed expiry (continuing professional education required for maintenance)
Exam Price:USD 575 (non-member, estimated ISACA standard pricing range)
Passing Score:Typically 65% (ISACA standard scaled scoring may apply)
Available Languages:English
Related Certifications:CGEIT
CISM
CRISC
Exam Format:Multiple choice
Real Exam Qty:100 (estimated)
Sample Questions:ISACA AAIR Sample Questions
Exam Way:Online and testing center delivery options (computer-based exam)
Pre Condition:Recommended: familiarity with IT governance, risk management, and AI/ML fundamentals; related ISACA certifications beneficial
Official Syllabus URL:https://www.isaca.org

ISACA AAIR Exam Syllabus Topics:

SectionObjectives
Topic 1: Regulatory and Compliance Requirements- Global AI regulatory landscape
  • 1. Industry standards for AI risk management
    • 2. Data protection and privacy regulations
      Topic 2: AI Risk Management- Risk identification and assessment for AI systems
      • 1. Model risk identification
        • 2. Operational risk in AI deployment
          Topic 3: Ethics, Privacy, and Responsible AI- Ethical AI principles and compliance
          • 1. Transparency and explainability
            • 2. Bias and fairness mitigation
              Topic 4: AI Governance and Strategy- AI governance frameworks and organizational oversight
              • 1. Policy development for AI systems
                • 2. Roles and responsibilities in AI governance
                  Topic 5: AI Lifecycle Controls- Controls across AI development lifecycle
                  • 1. Model validation and testing
                    • 2. Data quality and preparation controls

                      ISACA Advanced in AI Risk Sample Questions:

                      Question #1

                      Which of the following BEST helps to ensure adherence to data minimization principles when using an AI model whose training dataset contains personal information?

                      • A. Data loss prevention (DLP)
                      • B. Role-based access control (RBAC)
                      • C. Data encryption
                      • D. Pseudonymization
                      Answer: D

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                      Question #2

                      An organization is integrating AI systems into core business operations and has decided to establish a formal process to align AI initiatives with corporate values. Which of the following is the GREATEST benefit of this decision?

                      • A. Ethical principles can be added to AI development and usage after deployment.
                      • B. The transparency and explainability of AI model decisions is enhanced for all stakeholder groups.
                      • C. Return on investment (ROI) for new AI services can be evaluated more accurately.
                      • D. Executive support for technical training and upskilling related to AI can be more effectively obtained.
                      Answer: B

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                      Question #3

                      Which of the following is the PRIMARY benefit of integrating AI risk processes into an enterprise risk framework?

                      • A. Improved compliance with regulatory requirements
                      • B. More accurate benchmarking of AI key performance indicators (KPIs)
                      • C. Rapid identification of cyber threats and risks
                      • D. Organization-level oversight and strategic alignment
                      Answer: D

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                      Question #4

                      An organization deploys an AI credit scoring model trained on historical financial data that underrepresents certain demographic groups. Which of the following is the risk practitioner ' s BEST recommendation to mitigate this risk?

                      • A. Notify stakeholders that the model may not always reflect standard loan approval thresholds.
                      • B. Implement reporting for model drift and anomalous model decisions.
                      • C. Use unsupervised learning to identify hidden or complex discriminatory patterns in the dataset.
                      • D. Define specific inclusivity goals and expand data to a broader range of sources.
                      Answer: D

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                      Question #5

                      Which of the following is the MOST important consideration when determining mitigation controls for an AI system?

                      • A. Prioritizing controls based on the complexity and computational requirements of the AI system
                      • B. Providing comprehensive AI risk awareness training to security and technical personnel
                      • C. Determining control performance baselines and reporting requirements for regulatory compliance
                      • D. Evaluating control effectiveness and costs against potential business losses from unmitigated AI risk
                      Answer: D

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