ISACA AAIR Exam Details & Actual Exam Questions

  • Exam Code/Number: AAIR
  • Exam Name/Title: ISACA Advanced in AI Risk
  • Certification Provider: ISACA
  • Corresponding Certification: AI Risk
  • Exam Questions: 92
  • Updated On: Aug,14 2026
  • Certification Level: Professional

ISACA Advanced in AI Risk Exam Questions

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ISACA AAIR Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Risk (AAIR)
Exam Number:AAIR
Exam Price:USD 575 (non-member, estimated ISACA standard pricing range)
Exam Duration:120 minutes
Certificate Validity Period:No fixed expiry (continuing professional education required for maintenance)
Related Certifications:CRISC
CISM
CGEIT
Exam Format:Multiple choice
Passing Score:Typically 65% (ISACA standard scaled scoring may apply)
Available Languages:English
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 Lifecycle Controls- Controls across AI development lifecycle
              • 1. Model validation and testing
                • 2. Data quality and preparation controls
                  Topic 5: AI Governance and Strategy- AI governance frameworks and organizational oversight
                  • 1. Roles and responsibilities in AI governance
                    • 2. Policy development for AI systems


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