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AAISM Exam Objectives, AAISM Test Study Guide

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
Which of the following controls BEST mitigates the inherent limitations of generative AI models?

A. Reverse engineering the models
B. Adopting AI-specific regulations
C. Classifying and labeling AI systems
D. Ensuring human oversight

Answer: D

Explanation:
The AAISM governance framework emphasizes that the inherent limitations of generative AI-including hallucinations, bias, and unpredictability-are best mitigated by human oversight. Human-in-the-loop review ensures that outputs are validated before being used in sensitive or high-risk contexts. Regulatory adoption, system classification, and reverse engineering all play supporting roles but do not directly safeguard against the model's inherent unpredictability. Governance best practices highlight human oversight as the critical safeguard.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Human Oversight and Accountability) AI Security Management Study Guide - Mitigating Generative AI Limitations

NEW QUESTION # 28
Which of the following MOST effectively minimizes the attack surface when securing AI agent components during their development and deployment?

A. Implement compartmentalization with least privilege enforcement.
B. Deploy pre-trained models directly into production.
C. Consolidate event logs for correlation and centralized analysis.
D. Schedule periodic manual code reviews.

Answer: A

Explanation:
The most effective strategy tominimize attack surfacesin AI agent security is to apply compartmentalization and least privilege enforcement.
AAISM control frameworks emphasize:
* Isolation of components (e.g., training, inference, data pipelines) to limit lateral movement.
* Principle ofleast privilegeto restrict access only to what is required for function.
* Hardening AI pipelines through segmentation rather than relying solely on manual reviews or monitoring.
Pre-trained models and log centralization are useful but do not directly reduce the attack surface.Manual code reviewsare important but insufficient against runtime exploitation.
Thus,compartmentalization with least privilege enforcementis the most effective technical safeguard.

NEW QUESTION # 29
To ensure AI tools do not jeopardize ethical principles, it is MOST important to validate that:

A. The organization has implemented a responsible development policy
B. Stakeholders have approved alignment with company values
C. Outputs of AI tools do not perpetuate adverse biases
D. AI tools are evaluated by the privacy department before implementation

Answer: C

Explanation:
AAISM highlights that the core ethical risk in AI is the perpetuation of bias that results in unfair or discriminatory outcomes. Therefore, the most important validation step is ensuring that outputs of AI systems are free from adverse biases. A responsible development policy, stakeholder approvals, and privacy reviews all contribute to governance, but they do not directly ensure ethical outcomes. Validation of output fairness is the critical safeguard for ensuring AI does not violate ethical principles.
References:
AAISM Study Guide - AI Risk Management (Bias and Ethics Validation)
ISACA AI Security Management - Ethical AI Practices

NEW QUESTION # 30
Which of the following is the MOST serious consequence of an AI system correctly guessing the personal information of individuals and drawing conclusions based on that information?

A. The exposure of personal information may lead to a decline in public trust
B. The exposure of personal information may result in litigation
C. The publicly available output of the model may include false or defamatory statements about individuals
D. The output may reveal information about individuals or groups without their knowledge

Answer: D

Explanation:
The AAISM curriculum states that the most serious privacy concern occurs when AI systems infer and disclose sensitive personal or group information without the knowledge or consent of the individuals. This constitutes a direct breach of privacy rights and data protection principles, including those enshrined in GDPR and other global regulations. While litigation, reputational damage, or loss of trust are significant consequences, the unauthorized revelation of personal information through inference is classified as the most severe, because it directly undermines individual autonomy and confidentiality.
References:
AAISM Exam Content Outline - AI Risk Management
AI Security Management Study Guide - Privacy and Confidentiality Risks

NEW QUESTION # 31
The PRIMARY benefit of implementing moderation controls in generative AI applications is that it can:

A. Filter out harmful or inappropriate content
B. Increase the model's ability to generate diverse and creative content
C. Ensure the generated content adheres to privacy regulations
D. Optimize the model's response time

Answer: A

Explanation:
AAISM materials identify the primary benefit of moderation controls in generative AI systems as their ability to filter out harmful, offensive, or inappropriate content before it is delivered to users. This safeguards organizational reputation, compliance, and user trust. While moderation may indirectly support compliance with privacy requirements, its main function is ensuring that outputs align with ethical and safety standards.
Moderation does not enhance creativity or response speed. Its primary value is in controlling the quality of generated outputs by blocking harmful content.
References:
AAISM Study Guide - AI Technologies and Controls (Moderation and Output Controls) ISACA AI Security Management - Harmful Content Mitigation in Generative AI

NEW QUESTION # 32
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