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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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.

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2026 AAISM New Dumps Free | Pass-Sure Exam AAISM Blueprint: ISACA Advanced in AI Security Management (AAISM) Exam 100% Pass

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
When designing self-driving vehicles, conformity assessment is PRIMARILY needed to:

Answer: D

Explanation:
Conformity assessment is primarily conducted to evaluate whether an AI system complies with applicable regulations, standards, and governance frameworks. For self-driving vehicles, this is critical because they are high-risk AI systems subject to strict regulatory and safety requirements.


NEW QUESTION # 42
Which of the following metrics BEST evaluates the ability of a model to correctly identify all true positive instances?

Answer: D

Explanation:
AAISM technical coverage identifies recall as the metric that specifically measures a model's ability to capture all true positive cases out of the total actual positives. A high recall means the system minimizes false negatives, ensuring that relevant instances are not overlooked. Precision instead measures correctness among predicted positives, specificity focuses on true negatives, and the F1 score balances precision and recall but does not by itself indicate the completeness of capturing positives. The official study guide defines recall as the most direct metric for evaluating how well a model identifies all relevant positive cases, making it the correct answer.


NEW QUESTION # 43
An organization plans to use an open-source foundational AI model. Which of the following is MOST important for the AI governance committee to consider when approving its use?

Answer: B

Explanation:
AAISM emphasizes that open-source AI models present elevated data leakage risks because internal data may flow into external, uncontrolled repositories or be used for further training. Governance bodies must prioritize the risk of data exposure, model reuse, data retention uncertainty, and uncontrolled model behavior.
While accuracy (B) and support (C) are important operational considerations, they are not the primary governance risk. Employee privacy rights (D) matter but are encompassed within the broader risk of data leakage.
References: AAISM Study Guide - Open-Source AI Governance; Data Leakage and Uncontrolled Model Behavior.


NEW QUESTION # 44
Which of the following is the GREATEST contributor to end user security risk associated with generative AI?

Answer: A

Explanation:
End users are a primary interface with generative AI, and without sufficient awareness training they may unknowingly misuse the technology, expose sensitive data, or fall victim to AI-enabled threats, making this the largest contributor to security risk.


NEW QUESTION # 45
Embedding unique identifiers into AI models would BEST help with:

Answer: B

Explanation:
The AAISM framework explains that embedding unique identifiers--such as digital watermarks or model fingerprints--enables organizations to trace and verify model provenance. This technique is used for tracking ownership and intellectual property rights over models, particularly when sharing, licensing, or distributing AI systems. While identifiers may support certain security functions, their primary control objective is ownership verification, not preventing access, bias removal, or adversarial detection. The correct alignment with AAISM controls is tracking ownership.


NEW QUESTION # 46
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