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| Section | Objectives |
|---|---|
| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| AI Risk Management | - Risk identification and assessment for AI systems
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| AI Lifecycle Controls | - Controls across AI development lifecycle
|
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NEW QUESTION # 33
Which of the following BEST mitigates risk associated with evasion attacks on AI models?
Answer: A
Explanation:
Evasion attacks involve adversaries crafting inputs specifically designed to fool AI models into producing incorrect outputs-for example, manipulating images to evade object detection or modifying text to bypass content classifiers. Detecting these attacks requires identifying inputs that are statistically unusual or inconsistent with legitimate use patterns.
Why B is Correct: The ISACA AAIR adversarial AI security guidance identifies anomaly detection as the most effective mitigation for evasion attacks. Anomaly detection systems monitor input distributions, model query patterns, and output characteristics for statistical deviations that indicate adversarial manipulation. By identifying inputs that fall outside expected distributions or trigger unusual model responses, anomaly detection catches evasion attempts before they produce harmful outputs.
Why A is Wrong: API rate limiting controls query frequency to prevent brute-force model probing but does not detect or prevent crafted adversarial inputs sent at normal rates. An attacker can evade rate limits by spacing requests or distributing queries.
Why C is Wrong: Predictive analytics uses historical patterns to forecast future outcomes. It does not specifically detect real-time adversarial manipulation of model inputs.
Why D is Wrong: Feature importance weighting adjusts how much different input features influence model predictions. While it can improve robustness to irrelevant features, it does not detect adversarial inputs specifically crafted to exploit important features.
NEW QUESTION # 34
Which of the following is the MOST suitable key risk indicator (KRI) for model drift resulting in biased or discriminatory outputs?
Answer: B
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. A disparate impact ratio directly measures differences in favorable outcomes across groups and is therefore the most relevant KRI when the stated concern is discriminatory output. Population stability measures distributional drift but does not directly establish disparate treatment. This makes option C, Disparate impact ratio, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 35
An organization deploys an autonomous system that makes decisions affecting compliance with regulations.
If those decisions could potentially produce regulatory breaches, which of the following BEST helps to manage associated liability exposures?
Answer: C
NEW QUESTION # 36
A social media platform uses AI tools to automate real-time content moderation. Which of the following would be of GREATEST concern to a risk practitioner?
Answer: B
Explanation:
Within the ISACA Advanced in AI Risk framework, governance decisions should align AI use with policy, accountability, stakeholder expectations, risk appetite, and applicable legal or ethical obligations. Automated content moderation can falsely restrict users, so a meaningful appeal process is essential for contestability, human review, and correction of erroneous decisions. Compute cost and reinforcement learning are not the primary governance concern. This makes option D, Lack of an appeal process for users falsely accused of violating terms of service, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 37
A risk practitioner is developing new risk scenarios related to an organization ' s use of AI tools to predict industrial equipment failures. Which of the following is MOST important for the risk practitioner to verify?
Answer: C
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Predictive-maintenance models can become unreliable when equipment behavior, operating conditions, or sensor relationships change. The practitioner should verify that the model can adapt to changing operational conditions rather than merely checking software version or end-user training effort. This makes option C, The model is capable of adjusting quickly to changes in operational conditions, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 38
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