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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - AI governance communication and reporting - AI risk assessment and treatment strategies - AI risk monitoring and continuous improvement - Enterprise AI risk program design |
| AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases |
| AI Life Cycle Risk Management | - AI model and data risk identification - AI bias, drift, transparency, and control evaluation - AI development, deployment, and monitoring risks |
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NEW QUESTION # 172
Which of the following is the MOST suitable key risk indicator (KRI) for model drift resulting in biased or discriminatory outputs?
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. 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 # 173
Which control is MOST effective to prevent attackers from embedding malicious instructions within user prompts to alter model 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. Prompt injection is best addressed at runtime by validating incoming content and controlling model outputs. RBAC limits permissions, encryption protects stored data, and static filters may miss context- dependent attacks, while content validation directly addresses malicious instructions. This makes option D, Content validation, 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 # 174
Which of the following is the PRIMARY benefit of aligning AI risk management with existing organizational governance frameworks?
Answer: C
Explanation:
Organizational governance frameworks provide the structures, processes, and oversight mechanisms through which enterprises manage their activities and risks. Aligning AI risk management with these frameworks ensures AI activities receive the same level of strategic oversight as other organizational functions.
Why C is Correct: The ISACA AAIR curriculum identifies enterprise-level oversight and strategic alignment as the primary benefit of governance framework integration. When AI risk management operates within established governance structures, AI decisions are subject to the same approval authorities, risk escalation pathways, and strategic alignment checks that govern all major organizational decisions. This produces coherent, enterprise-aware AI governance.
Why A is Wrong: Role development and responsibility clarification are governance activities that may result from alignment, but they represent structural outputs rather than the primary benefit. The benefit is the oversight quality, not the organizational structure itself.
Why B is Wrong: Expediting compliance approvals is an efficiency benefit that may arise from better- organized governance. However, speed of approval is not the primary purpose of framework alignment-the purpose is quality and consistency of oversight.
Why D is Wrong: Standardizing acquisition processes is a procurement function benefit. While governance alignment may improve procurement consistency, standardization is a narrow operational benefit compared to the strategic oversight value of full governance integration.
NEW QUESTION # 175
An organization is designing an enterprise dashboard to support governance of its AI program. Which of the following is the risk practitioner's BEST recommendation?
Answer: C
Explanation:
An enterprise AI governance dashboard must provide decision-makers with a comprehensive, integrated view of AI program health across all dimensions-risk, performance, compliance, ethics, and operations.
Fragmenting this view or focusing on narrow metrics produces an incomplete governance picture.
Why B is Correct: The ISACA AAIR governance reporting guidance recommends aggregating diverse metrics from all AI life cycle stages as the best approach for an enterprise governance dashboard. This comprehensive aggregation enables decision-makers to see the full AI risk and performance picture-from data quality in training through deployment performance, bias monitoring, security incidents, and compliance status-in a single, actionable view. This unified perspective supports informed enterprise-level governance decisions.
Why A is Wrong: Uptime and availability metrics are operational infrastructure indicators that represent only one dimension of AI governance. Focusing primarily on availability misses critical governance concerns including model fairness, accuracy, bias, and ethical compliance.
Why C is Wrong: Risk heat maps based solely on training variance are narrow technical performance indicators. A governance dashboard requires breadth across risk types and life cycle stages, not depth on one specific technical metric.
Why D is Wrong: Assigning dashboard responsibility exclusively to IT centralizes governance reporting in one function that may lack visibility into business risk, ethical compliance, and strategic alignment dimensions of AI governance. Enterprise dashboards require cross-functional input and ownership.
NEW QUESTION # 176
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: A
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 # 177
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