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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 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 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 (Q127-Q132):

NEW QUESTION # 127
As organizations increasingly rely on vendors to develop AI systems, which of the following is the MOST effective way to monitor vendors and ensure compliance with ethical and security standards?

Answer: C

Explanation:
AAISM vendor governance guidance identifies regular audits of vendor processes as the most effective method of ensuring compliance with ethical and security standards. Independent audits provide verifiable assurance that vendors are meeting agreed-upon requirements. Self- attestation, internal monitoring, or documentation sharing provide some transparency but do not guarantee compliance. The best practice, particularly for high-risk AI deployments, is independent and recurring audits of vendor processes.


NEW QUESTION # 128
Which of the following is the GREATEST benefit of performing AI security risk assessments?

Answer: B

Explanation:
AAISM emphasizes that the core outcome of AI risk assessments is prioritization: mapping threat likelihood and business impact to determine which risks to treat first, at what strength, and with which controls. Implementing privacy controls (A), funding alignment (B), and updating registers (C) are important outputs, but the greatest benefit is making defensible, prioritized decisions that align with risk appetite and optimize control selection and resource allocation.
References: AI Security Management (AAISM) Body of Knowledge - AI Risk Assessment & Treatment; Risk Appetite, Tolerance, and Prioritization; Governance of Risk Decisions and Tracking.


NEW QUESTION # 129
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 # 130
An organization decides to use an anomaly-based intrusion detection system (IDS) integrated with a generative adversarial network (GAN)-enabled AI tool. The integrated tool would MOST effectively detect intrusions by leveraging:

Answer: B

Explanation:
AAISM materials describe that GAN-based systems excel at generating synthetic data-including simulated attack traffic-which can significantly enhance anomaly-based intrusion detection capabilities. The guidance emphasizes that synthetic attack samples help strengthen the model's ability to detect rare or emerging intrusion types. This aligns with the principle that AI security controls should leverage adversarially generated data during training to improve resilience.
Options A and C describe generic ML enhancements, but not GAN-specific advantages. Option B is useful but insufficient for anomaly detection, which relies heavily on recognizing atypical, previously unseen patterns.
References: AAISM Study Guide - AI Security Controls; Adversarial Simulation and Synthetic Data Generation.


NEW QUESTION # 131
A global organization experienced multiple incidents of staff pasting confidential data into public chatbots. Which action is MOST important to reduce short-term risk?

Answer: A

Explanation:
AAISM states that the most effective short-term mitigation for unintentional data leakage into public AI tools is targeted, role-based AI security awareness, focused on:
- what data cannot be entered
- consequences of leakage
- real-world scenarios employees face
An acceptable-use policy (C) is necessary but insufficient alone. Blocking LLMs (D) may reduce access but does not change user behavior and may cause shadow AI usage. Generic phishing training (B) does not address AI misuse risks.


NEW QUESTION # 132
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