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NEW QUESTION # 321
Which of the following would BEST help an organization align its AI initiatives with business objectives?
Answer: A
Explanation:
An AI governance committee provides cross-functional oversight to align AI strategy, investment, and risk appetite with business goals. It sets policies, prioritizes portfolios, ensures accountability, and integrates compliance, ethics, and security into decision-making. While compliance, ethics, and information protection are essential, governance is the primary mechanism that systematically connects AI initiatives to enterprise objectives.
References: AI Security Management™ (AAISM) Body of Knowledge: AI Governance Operating Model- Structures, Roles, and Decision Rights; AAISM Study Guide: Strategic Alignment, Portfolio Oversight, and Accountability Mechanisms.
NEW QUESTION # 322
Which of the following is the GREATEST benefit of performing AI security risk assessments?
Answer: A
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 # 323
During the creation of a new large language model (LLM), an organization procured training data from multiple sources. Which of the following is MOST likely to address the CISO's security and privacy concerns?
Answer: D
Explanation:
AAISM guidance highlights data minimization as a critical practice for addressing both security and privacy concerns. By ensuring that only the minimum necessary data is collected and retained, the organization reduces the risk of sensitive information being exposed or misused during training. Data augmentation expands data but does not mitigate privacy risk. Classification organizes data but does not limit exposure.
Data discovery helps locate sources but does not directly reduce risks. The control that directly aligns with privacy-by-design principles is data minimization.
References:
AAISM Exam Content Outline - AI Risk Management (Data Privacy and Minimization) AI Security Management Study Guide - Privacy Safeguards in AI Training
NEW QUESTION # 324
A security operation center (SOC) analyst detects repetitive API calls sending inputs to an AI solution. Which of the following attacks has MOST likely occurred?
Answer: D
Explanation:
Repetitive API calls with systematically crafted inputs are characteristic of attempts to query a model extensively to reconstruct its behavior or parameters, which is indicative of a model extraction attack.
NEW QUESTION # 325
A large corporation has received an influx of sophisticated credential-phishing emails and wants to leverage an AI solution to detect and quarantine these messages before they reach employees. Which of the following blue-team AI features is BEST suited to this task?
Answer: D
Explanation:
For pre-delivery phishing detection and classification, the most appropriate capability is NLP- tokenization, feature extraction, semantic similarity, and supervised classifiers (e.g., transformer-based classifiers) tuned on phishing corpora and indicators. NLP models score messages and drive automated quarantine policies. An LLM (Option A) is a model type, not a specific blue-team feature; NLG (Option C) is for generation, not detection; RAG (Option D) augments responses with retrieved knowledge but does not by itself optimize classification and quarantine of phishing emails.
References:
AAISM Body of Knowledge: Defensive AI Use Cases; Text Classification Pipelines for Security Operations; Email Security and AI-Driven Triage.
AAISM Study Guide: NLP for Threat Detection; Model Evaluation for Precision/Recall in Security Classifiers; SOC Integration and Automated Containment.
NEW QUESTION # 326
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