ISACA AAIR Exam Information and Actual 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: Sep 13, 2026

AAIR
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ISACA
AAIR Exam
ISACA Advanced in AI Risk

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ISACA AAIR Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Risk (AAIR) Certification Exam
Exam Number:AAIR
Exam Price:USD 459 (member) / USD 599 (non-member)
Exam Duration:150 minutes
Related Certifications:CGRC
CISSP
CRISC
CISM
CISA
CGEIT
CDPSE
CRMA
Real Exam Qty:90
Available Languages:Chinese (Simplified), Spanish, English
Exam Format:Multiple-choice, Proctored exam (test center or remote where available), Computer-based testing
Recommended Training:AAIR Virtual Workshop (ISACA Training)
AAIR QAE Practice Database (ISACA Learning Resources)
AAIR Online Review Course (ISACA)
Exam Registration:ISACA Certification Registration (MyISACA portal)
ISACA AAIR Official Certification Page
Sample Questions:ISACA AAIR Sample Questions
Exam Way:Computer-based exam via PSI testing centers or remote proctoring (availability varies by region; some regions require test center only).
Pre Condition:Candidates must hold one of ISACA or equivalent recognized certifications such as CISA, CISM, CRISC, CGEIT, CDPSE, CISSP, CRMA, CGRC, or other approved risk/security/audit designations, and have relevant IT risk or advisory experience.
Official Syllabus URL:https://www.isaca.org/credentialing/aair

ISACA AAIR Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Risk Program Management42%- AI risk monitoring and continuous improvement
- AI risk assessment and treatment strategies
- Enterprise AI risk program design
- AI governance communication and reporting
Topic 2: AI Risk Governance and Framework Integration37%- AI Organizational Processes and Alignment
- AI Ownership, Oversight, and Accountability
- AI Models, Frameworks, Strategies, and Use Cases
Topic 3: AI Life Cycle Risk Management- AI model and data risk identification
- AI development, deployment, and monitoring risks
- AI bias, drift, transparency, and control evaluation


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