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| Section | Weight | Objectives |
|---|---|---|
| AI Life Cycle Risk Management | 21% | - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation - AI Design, Development/Procurement, and Documentation - AI Data and Asset Management |
| AI Risk Program Management | 42% | - AI Risk Response and Mitigation - AI Risk Monitoring and Reporting - AI Risk Identification and Assessment - AI Risk Assurance and Continuous Improvement |
| AI Risk Governance and Framework Integration | 37% | - AI Regulatory Compliance and Legal Considerations - AI Organizational Processes and Alignment - AI Trustworthiness, Ethical and Societal Implications - AI Ownership, Oversight, and Accountability - AI Policies, Procedures, and Organizational Training - AI Models, Frameworks, Strategies, and Use Cases |
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NEW QUESTION # 84
Which of the following is the BEST way for a risk practitioner to communicate rapidly evolving changes to the organization ' s risk profile resulting from the use of generative AI?
Answer: A
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. Rapidly changing generative-AI risk is best communicated through concrete real-world incidents and business impacts that stakeholders can understand. Technical root causes or exhaustive lists can obscure the practical consequences that decision-makers need to evaluate. This makes option B, Provide real-world examples of risk impacts from generative AI incidents and breaches, 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 # 85
Which of the following is the BEST governance approach for balancing risk management and operational flexibility across diverse AI applications?
Answer: A
Explanation:
AI governance across diverse applications requires frameworks flexible enough to accommodate varying risk profiles, regulatory environments, and operational contexts while maintaining consistent governance standards. Rigid or overly centralized approaches reduce operational effectiveness.
Why B is Correct: The ISACA AAIR framework advocates for adaptable governance frameworks that can be scaled and tailored to specific AI use cases. A risk-based, adaptable framework applies more rigorous controls to high-risk applications while allowing operational flexibility for lower-risk uses. This balance enables innovation while maintaining appropriate risk oversight-a core principle of proportionate AI governance.
Why A is Wrong: Single-regulation compliance focus creates compliance tunnel vision that may miss material risks not covered by that regulation. AI governance must address the full risk landscape, not just one regulatory framework.
Why C is Wrong: External consultants provide periodic independent assurance, not governance. Relying on external reviews for governance would be episodic rather than continuous, creating governance gaps between review cycles.
Why D is Wrong: Centralized decision-making creates operational bottlenecks and slows AI deployment.
Effective governance delegates decision authority appropriately while maintaining oversight, rather than centralizing all decisions in a single function.
NEW QUESTION # 86
Which of the following is the PRIMARY purpose of applying thorough cleansing and normalization to AI training data?
Answer: C
Explanation:
Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems. Cleansing and normalization reduce inconsistent, erroneous, or statistically distorted training data that can produce biased or inaccurate model behavior. Their primary value is improving trustworthy inputs rather than lowering compute cost or satisfying privacy law by themselves. This makes option C, Addressing statistical distortions in training data that could cause model bias, 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 # 87
Which of the following BEST mitigates risk associated with evasion attacks on AI models?
Answer: C
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 # 88
Which of the following should be a risk practitioner's PRIMARY consideration when developing risk scenarios related to adversarial manipulation of a business-critical AI system?
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. Risk scenarios for adversarial manipulation should be classified using criteria aligned with organizational tolerance so likelihood and impact can drive proportionate treatment decisions. Attack prevalence and patch frequency are inputs, not the governing risk criterion. This makes option C, Alignment of risk classification criteria with organizational tolerance, 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 # 89
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