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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability |
| Topic 2: AI Risk Program Management | 42% | - AI governance communication and reporting - Enterprise AI risk program design - AI risk monitoring and continuous improvement - AI risk assessment and treatment strategies |
| Topic 3: AI Life Cycle Risk Management | - AI model and data risk identification - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation |
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NEW QUESTION # 96
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 # 97
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: D
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 # 98
Which of the following is the MOST important objective when selecting AI controls based on dataset evaluation and validation?
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. Dataset validation controls should protect the fairness and accuracy of model behavior by ensuring data is suitable, representative, and reliable. Real-time recalibration, framework alignment, and explainability monitoring are useful but do not replace this core objective. This makes option C, Preserving model fairness and accuracy, 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 di rectly. 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 # 99
An organization has deployed generative AI tools broadly but lacks a consistent method to refresh governance policies and controls. Which of the following is the risk practitioner's BEST recommendation?
Answer: B
Explanation:
Generative AI capabilities and the associated risk landscape evolve rapidly. Governance policies and controls must be refreshed through a structured, regular process rather than reactively or only when compliance requirements change.
Why A is Correct: According to ISACA AAIR, establishing a regular review cadence with codified reassessment procedures is the most robust approach because it creates a systematic, predictable process for keeping governance current. By documenting when and how policies will be reviewed-including triggers for ad hoc review (new deployments, incidents, regulatory changes)-the organization ensures governance never stagnates regardless of external pressures.
Why B is Wrong: Regulatory alignment is an important input to governance refresh but represents a reactive, external-trigger approach. Relying primarily on regulatory signals means governance lags behind organizational AI changes not covered by new regulations.
Why C is Wrong: Centralizing authority in executive and technical leadership creates decision bottlenecks and reduces the operational agility needed to keep pace with rapidly evolving AI deployments. Distributed governance with clear escalation paths is more effective.
Why D is Wrong: Annual reviews are too infrequent for generative AI tools, which may see significant capability changes and risk profile shifts multiple times per year. Annual compliance audits cannot keep governance current in a rapidly evolving AI environment.
NEW QUESTION # 100
Which of the following is the PRIMARY benefit of establishing a cross-functional AI governance committee?
Answer: D
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. A cross- functional AI governance committee coordinates legal, privacy, security, business, data, and operational perspectives so AI use is managed consistently across the enterprise. Version control or individual human review are specific activities rather than the committee ' s primary benefit. This makes option D, Coordinated management of AI use risk, 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 # 101
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