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| Section | Weight | Objectives |
|---|---|---|
| AI Governance and Program Management | 31% | - Stakeholder considerations and governance frameworks
|
| AI Technologies and Controls | 38% | - AI security monitoring and operations
|
| AI Risk Management | 31% | - AI threat and vulnerability management
|
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NEW QUESTION # 139
The BEST reason for inviting independent reviewers to evaluate the output of an AI application is to:
Answer: A
Explanation:
Independent reviewers provide an external perspective that helps identify and reduce biases that internal teams may overlook due to familiarity or shared assumptions, leading to more objective evaluation of AI outputs.
NEW QUESTION # 140
An organization's CIO provided the AI steering committee with a list of AI technologies in use and tasked them with categorizing the technologies by risk. Which of the following should the committee do FIRST?
Answer: B
Explanation:
AAISM governance practices state that before categorizing technologies by risk, the first step is to ensure that all AI systems are documented in the organizational asset inventory. A complete inventory provides the foundation for subsequent risk analysis, accountability, and governance. Grouping solutions, identifying vulnerabilities, and assessing risk levels come afterward, once inventory accuracy is established. Without confirming that the technologies are recorded in the inventory, risk categorization may miss critical assets.
References:
AAISM Study Guide - AI Governance and Program Management (AI Inventories as a Prerequisite to Risk Analysis) ISACA AI Security Management - Asset Visibility and Risk Categorization
NEW QUESTION # 141
Which of the following is the MOST important course of action to ensure an AI system produces ethical results?
Answer: D
Explanation:
Embedding responsible practices throughout the AI life cycle ensures that ethical considerations are continuously addressed during design, development, deployment, operation, and retirement.
This approach systematically promotes fairness, accountability, transparency, privacy, and risk management across the entire AI system lifecycle.
NEW QUESTION # 142
AI developers often find it difficult to explain the processes inside deep learning systems PRIMARILY because:
Answer: A
Explanation:
Deep learning models learn high-dimensional, non-linear representations through layered parameterization that resists simple causal narratives. The internal mechanisms (e.g., distributed feature representations and complex statistical transformations) are difficult to map to human-interpretable rules, making explanation challenging. This is the primary reason for explainability difficulty in deep learning.
Option A addresses data origin/volatility, not explainability. Option B mischaracterizes model behavior as mutable "knowledge" without logs. Option C notes probabilistic foundations but that alone does not make systems inexplicable.
References: AI Security Management (AAISM) Body of Knowledge: "Explainability and Interpretability- Complexity in Deep Learning," "Model Behavior, Surrogates, and Post-hoc Explanations"; AAISM Study Guide: "Interpreting High-Dimensional Representations," "Limits of Transparency in Neural Architectures."
NEW QUESTION # 143
An organization has discovered that employees have started regularly utilizing open-source generative AI without formal guidance. Which of the following should be the CISO's GREATEST concern?
Answer: A
Explanation:
The greatest immediate risk from unsanctioned use of public or open-source generative AI tools is data leakage-employees may paste confidential or regulated information into third-party systems, resulting in loss of confidentiality, regulatory exposure, and loss of intellectual property. AAISM emphasizes that when AI use occurs outside approved channels, the top control priority is preventing exfiltration of sensitive data via prompts, attachments, and context sharing. Monitoring and policy are necessary enablers, but leakage is the highest-impact failure mode in the short term; hallucinations primarily affect accuracy, not confidentiality.
References:* AI Security Management™ (AAISM) Body of Knowledge: Generative AI governance; human- in-the-loop risks; data loss and exfiltration vectors in prompts; sanctioned vs. unsanctioned AI usage.* AI Security Management™ Study Guide: Immediate risk triage for shadow AI; DLP and input-control safeguards; confidentiality-first posture for generative AI adoption.
NEW QUESTION # 144
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