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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.
Topic 2
  • AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 3
  • AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q99-Q104):

NEW QUESTION # 99
To ensure the ethical and responsible use of AI, which of the following AI usage policy metrics is MOST important for an organization to monitor?

Answer: C

Explanation:
AAISM emphasizes governance effectiveness metrics tied to real lifecycle checkpoints. The count (and percentage) of AI projects that completed policy compliance review before deployment is a leading indicator of policy enforcement and assurance. It directly reflects whether responsible-AI requirements (risk assessment, impact assessment, data/privacy checks, security controls) are embedded in practice.


NEW QUESTION # 100
An incident response team has restored an AI system after a confirmed security incident. Which of the following should be done NEXT?

Answer: C

Explanation:
After restoration, the immediate next step is to ensure the AI system is functioning correctly and producing expected, reliable outputs. This confirms that the system has been fully and safely recovered before moving on to communication or further analysis.


NEW QUESTION # 101
An e-commerce platform has been subjected to false product image submissions and reviews, as well as fake search and purchase histories. Which of the following is the information security team's BEST course of action in this situation?

Answer: D

Explanation:
Verifying data provenance is the best course of action because it helps confirm the origin, authenticity, and integrity of submitted images, reviews, and behavioral data. This directly addresses the risk of malicious or fabricated data contaminating the platform and influencing AI- driven recommendations or analytics.


NEW QUESTION # 102
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?

Answer: C

Explanation:
AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems.
A certificate of destruction ensures:
- proof of lawful data disposal
- auditability
- regulatory compliance
- defensibility during inspections


NEW QUESTION # 103
Which of the following is BEST for analyzing true positives, true negatives, false positives, and false negatives produced by an AI model?

Answer: C

Explanation:
A confusion matrix is explicitly defined in AAISM as the framework used to interpret classification performance by listing:
* true positives
* true negatives
* false positives
* false negatives
Precision (B) and recall (D) are derived metrics that use parts of the matrix but do not show the full picture.
Hyperparameter tuning (A) is unrelated.
References: AAISM Study Guide - AI Model Evaluation Metrics; Confusion Matrix.


NEW QUESTION # 104
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