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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Program Management | 42% | - AI risk assessment and treatment strategies - AI risk monitoring and continuous improvement - AI governance communication and reporting - Enterprise AI risk program design |
| Topic 2: AI Risk Governance and Framework Integration | 37% | - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment |
| 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 # 163
An aviation operator is integrating AI into flight operations and maps existing controls to identified AI risk.
Which of the following is the risk practitioner ' s BEST course of action?
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. Once controls are mapped to AI risks, a control gap assessment determines whether existing measures are sufficient and identifies the difference between current and required control states. Performance targets or automatic upgrades should not precede that assessment. This makes option C, Perform a control gap assessment between current and desired AI control environments, 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 # 164
Which of the following is the PRIMARY benefit of incorporating new AI-specific controls?
Answer: A
Explanation:
AI systems introduce new categories of risk-model drift, adversarial attacks, algorithmic bias, hallucination-that conventional IT controls were not designed to address. AI-specific controls must complement existing controls to create comprehensive coverage across both traditional and emerging risk domains.
Why C is Correct: The ISACA AAIR curriculum identifies the holistic, comprehensive coverage of both conventional governance exposures and emerging AI vulnerabilities as the primary benefit of AI-specific controls. By designing controls that address AI-unique risks while integrating with existing governance structures, organizations achieve end-to-end risk management without creating coverage gaps between the old and new control environments.
Why A is Wrong: Compliance reporting prioritization is a governance administration activity. While AI- specific controls may clarify compliance requirements, identifying and prioritizing reporting requirements is not the primary purpose of implementing new controls.
Why B is Wrong: Cost reduction through control consolidation is an efficiency benefit that may result from control rationalization but is not the primary benefit of incorporating AI-specific controls. Adding necessary controls may actually increase costs in the short term.
Why D is Wrong: Accelerating deployment through efficient pre-deployment analysis is an operational efficiency benefit. The primary governance purpose of AI-specific controls is comprehensive risk coverage, not deployment speed.
NEW QUESTION # 165
In the context of AI risk mitigation, which of the following is the PRIMARY reason to have rollback capabilities in place for AI system updates?
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. Rollback capability allows the organization to restore a previously validated state when an AI update causes unintended consequences such as degraded accuracy, new bias, control failures, or unsafe behavior. It is a resilience and change-management safeguard. This makes option B, To correct unintended consequences, 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 # 166
A risk practitioner learns that an AI system used by a manufacturer for quality control (QC) has produced inaccurate responses that could potentially impact user safety. Which of the following is the risk practitioner's BEST recommendation to mitigate this risk?
Answer: A
Explanation:
When an AI system used for safety-critical quality control produces inaccurate responses that could harm users, the most immediate and effective safeguard is inserting human judgment into the decision process before unsafe outputs can reach production or end users.
Why A is Correct: The ISACA AAIR human oversight guidance identifies human-in-the-loop reviews as the most effective mitigation when AI outputs pose safety risks. In safety-critical applications like manufacturing quality control, human reviewers can catch and correct AI errors before they result in unsafe products reaching consumers. This control is immediately implementable, does not require model retraining, and directly addresses the safety risk. It is the appropriate response when AI accuracy cannot be fully trusted.
Why B is Wrong: Bias and fairness testing is a model evaluation activity that assesses whether outputs are systematically skewed. While useful for improving the model, it does not provide immediate protection against the safety risk of current inaccurate outputs.
Why C is Wrong: Synthetic data augmentation may improve model quality over time but requires model retraining and does not prevent currently inaccurate outputs from causing harm in the interim.
Why D is Wrong: Prompt engineering training improves how users interact with AI systems to elicit better outputs. It is useful for generative AI applications but does not directly address safety risks from QC system inaccuracies, which require operational oversight rather than improved prompting.
NEW QUESTION # 167
An organization plans to license an AI model trained on proprietary data to external partners. Which risk should the risk practitioner prioritize?
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. Licensing a model trained on proprietary information creates a material risk that confidential intellectual property may be memorized or surfaced in generated outputs. Encryption during training does not eliminate output leakage, and transparency concerns are secondary to direct IP exposure. This makes option D, Exposure of confidential intellectual property in generated content, 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 # 168
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