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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| AI Life Cycle Risk Management | - AI development, deployment, and monitoring risks - AI model and data risk identification - AI bias, drift, transparency, and control evaluation | |
| AI Risk Governance and Framework Integration | 37% | - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases |
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NEW QUESTION # 101
A risk practitioner is evaluating training datasets for a new AI model. Which of the following approaches BEST reduces fairness risk during model development?
Answer: D
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.
Representative sampling combined with explicit bias mitigation addresses both the root cause and manifestation of fairness risk. Label cleanup and synthetic data can help, but neither guarantees representative coverage or equitable outcomes by itself. This makes option D, Representative sampling strategies combined with bias mitigation controls, 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 # 102
A risk practitioner is developing risk scenarios related to successful data poisoning attacks on an AI model used across the organization. Which of the following is the BEST approach to help ensure the scenarios are relevant?
Answer: C
Explanation:
Risk scenario development in AI requires that scenarios be grounded in organizational context, business processes, and actual threat landscapes. Risk scenarios must reflect the specific systems, data flows, and stakeholder concerns relevant to the organization.
Why D is Correct: According to the ISACA AAIR Study Guide, engaging key stakeholders is the cornerstone of effective risk scenario development. Stakeholders bring domain knowledge, business context, and awareness of operational dependencies that technical practitioners may lack. This collaborative approach ensures scenarios address real-world consequences, organizational risk appetite, and business-critical functions-making them actionable and relevant.
Why A is Wrong: Adversarial testing in a sandbox validates controls but does not by itself produce contextually relevant risk scenarios. It is a technical activity, not a scenario development process.
Why B is Wrong: Peer benchmarking provides useful threat intelligence but cannot replace stakeholder engagement. Industry peer data may not reflect the organization's specific AI architecture or risk tolerance.
Why C is Wrong: Data flow diagrams are useful supporting artifacts but describe technical pathways rather than capturing the organizational and business context required for relevant risk scenarios.
NEW QUESTION # 103
Which of the following is the GREATEST risk when organizational risk tolerance is not embedded into AI policies?
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. If risk tolerance is not embedded in AI policies, different teams may make inconsistent decisions and permit impacts that exceed enterprise-approved limits. This weakens escalation, treatment, and acceptance decisions across the AI portfolio. This makes option C, Inconsistent governance decisions that fail to mitigate unacceptable impacts, 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 # 104
Which of the following BEST mitigates risk associated with evasion attacks on AI models?
Answer: D
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 # 105
A risk practitioner learns that a credit-scoring AI system is exhibiting bias that cannot be eliminated through further training. Which of the following is the risk practitioner's BEST recommendation?
Answer: D
Explanation:
Credit scoring AI systems are subject to anti-discrimination regulations that prohibit using models that produce biased outcomes affecting protected classes. When bias cannot be eliminated through technical means, continuing to operate the system creates ongoing legal violations and harm to affected individuals.
Why B is Correct: According to ISACA AAIR risk treatment guidance and legal compliance obligations, removing a biased credit-scoring system from production is the appropriate response when bias cannot be technically remediated. Continuing to operate a system known to produce discriminatory credit decisions violates anti-discrimination laws (such as the Equal Credit Opportunity Act), exposes the organization to regulatory enforcement, and causes ongoing harm to affected borrowers. Risk avoidance through system withdrawal is the appropriate treatment when the risk cannot be adequately mitigated.
Why A is Wrong: Requesting senior management risk acceptance for confirmed legal violations is inappropriate because organizations cannot accept risks involving known regulatory breaches. Senior management cannot legitimately authorize continued discriminatory lending practices.
Why C is Wrong: Sourcing a replacement system is a necessary future action but takes time to procure, validate, and deploy. In the interim, the biased system should not continue operating. Removing the system from production should precede replacement planning.
Why D is Wrong: Applying compensating controls to generate offsetting biases compounds the discriminatory problem rather than resolving it. Deliberately introducing additional bias-even in the opposite direction-creates an unpredictably biased model that does not produce fair outcomes.
NEW QUESTION # 106
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