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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q93-Q98):
NEW QUESTION # 93
Which of the following is the MOST effective action an organization can take to address data security risk when using generative AI features in an application?
Answer: B
Explanation:
AAISM directs organizations to manage third-party AI risks through contractual and technical controls that explicitly govern data use, retention, training/fine-tuning, isolation, and deletion. The most effective data- security action when consuming generative AI features is to require enforceable opt-out provisions that prohibit the provider from using the organization's data for training or secondary purposes and that mandate retention limits and secure deletion. Third-party audit reports (A) provide assurance but do not guarantee provider behavior for your specific data; awareness policies (B) are necessary but insufficient to control external processing; IP ownership guidelines (D) address legal rights, not data-security risk.
References: AI Security Management (AAISM) Body of Knowledge - Third-Party/Procurement Controls; Data Use & Retention Clauses; Training/Fine-tuning Opt-Out; Secure Deletion and Purpose Limitation.
NEW QUESTION # 94
Which of the following is the MOST likely cause of model drift?
Answer: B
Explanation:
Model drift occurs when the statistical properties of input data and/or the relationship between features and outcomes change over time, causing degraded model performance. The AAISM guidance classifies data- centric causes (distribution shift, concept drift, and contamination) as the primary drivers and highlights that malicious contamination of training or incremental learning data (data poisoning) is a direct, high-likelihood driver of observable drift in production because it changes the effective data-generating process the model learns from. In contrast:
* Perfect knowledge is an attacker capability descriptor, not a drift cause.
* Membership inference targets privacy of the training set and does not inherently shift data distributions.
* Model stealing targets IP/confidentiality; it does not change the victim model's data distribution or decision boundary in situ.
References:* AI Security Management™ (AAISM) Body of Knowledge: Model Risk & Drift; Data Integrity Risks; Adversarial ML-Poisoning vs. Evasion* AAISM Study Guide: Production Monitoring & Drift Management; Risk Scenarios-Data Poisoning Impacts and Controls* AAISM Mapping to Standards:
Lifecycle Risk Treatment-Robustness to Data Contamination; Continuous Monitoring and Feedback
NEW QUESTION # 95
Which of the following is the MOST effective use of AI-enabled tools in a security operations center (SOC)?
Answer: D
Explanation:
Themost effective SOC applicationof AI is indetecting subtle, hard-to-find attack patternsthat reduce false negatives.
AAISM technical control guidance notes that AI in SOCs is best applied to:
* Enhance detection accuracy and sensitivity to anomalies.
* Assist analysts in identifying hidden patterns that traditional rule-based systems miss.
* Augment-not replace-human decision-making for high-confidence outcomes.
Options B and C incorrectly shift responsibility entirely to AI, which contradicts governance principles requiringhuman oversight. Option D is useful for efficiency, but theprimary effectivenesscomes from improving detection quality.
Therefore, the most effective use is toreduce false negatives and detect subtle attacks.
NEW QUESTION # 96
Which of the following is the BEST way to reduce the risk of misuse of an AI agent that has access to critical data and systems?
Answer: D
Explanation:
AAISM frameworks highlight output-based controls, including output filtering, restriction validation, and policy-aligned guardrails as primary defenses for AI agents with high privileges. Ensuring the agent does not output unauthorized instructions or sensitive data directly mitigates misuse.
Allowing user configuration (B) increases risk. Prohibiting manipulation entirely (C) is impractical. Reducing human oversight (D) increases system abuse potential.
References: AAISM Study Guide - AI Agents, Output Controls, and Guardrails.
NEW QUESTION # 97
The PRIMARY purpose of adopting and implementing AI architecture as part of an organizational AI program is to:
Answer: B
Explanation:
An AI architecture, within program governance, exists to align AI system components and lifecycle processes with business goals and policy constraints. Architecture provides the organizing structure linking strategy, capabilities, processes, data, models, controls, and assurance so that AI outcomes are traceable to business value, risk appetite, and compliance expectations. Efficiency, speed, and threat analysis are important architectural qualities, but they are not the primary purpose; the primary purpose is strategic and governance alignment so that technical choices and controls consistently realize organizational objectives.
References:* AI Security Management™ (AAISM) Body of Knowledge: AI Program Architecture - alignment of capabilities, processes, and controls to business objectives* AI Security Management™ Study Guide: Architecture-driven governance, traceability from business goals to technical and control design
NEW QUESTION # 98
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