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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 Governance and Framework Integration | 37% | - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases - AI Regulatory Compliance and Legal Considerations - AI Trustworthiness, Ethical and Societal Implications - AI Policies, Procedures, and Organizational Training - AI Organizational Processes and Alignment |
| AI Risk Program Management | 42% | - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting - AI Risk Assurance and Continuous Improvement - AI Risk Response and Mitigation |
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NEW QUESTION # 179
An AI risk assessment identifies high-impact exploitable vulnerabilities in an AI model. No practical remediation controls are available to address these flaws before the planned go-live date.
What is the risk practitioner’s recommended risk response?
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. A high-risk model with known high-impact exploitable vulnerabilities should not be released when no effective fixes are available. Postponing launch is a risk-avoidance response that prevents knowingly introducing unacceptable exposure into production. This makes option D, Postpone the launch and communicate the delay to stakeholders, 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 # 180
Which of the following is the PRIMARY purpose of applying thorough cleansing and normalization to AI training data?
Answer: B
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 # 181
Which of the following is the MOST important benefit of deploying continuous monitoring and automated anomaly detection for AI models in production?
Answer: C
Explanation:
Production AI models face ongoing threats from adversarial attacks, unauthorized modifications, and parameter tampering. Continuous monitoring and automated anomaly detection provide real-time visibility into model behavior deviations that indicate security incidents or unauthorized changes.
Why C is Correct: The ISACA AAIR security monitoring guidance identifies timely detection of adversarial intrusions and unauthorized parameter changes as the most important benefit of continuous monitoring and automated anomaly detection. These security events directly threaten model integrity, potentially causing the model to make harmful decisions without the organization's knowledge. Timely detection enables rapid response before significant damage occurs-this is the highest-value security assurance outcome.
Why A is Wrong: Interpretability and transparency are model design properties that continuous monitoring supports through decision logging but cannot fundamentally improve. Transparency is achieved through model architecture and documentation choices, not monitoring.
Why B is Wrong: Strategic alignment is a governance and design objective. While monitoring can confirm outputs align with intended behavior, it cannot ensure alignment with evolving strategic goals, which requires governance review processes.
Why D is Wrong: Automated risk register and vulnerability database updates are administrative governance benefits that flow from monitoring findings. They represent a useful secondary capability but not the primary security value of continuous monitoring in production.
NEW QUESTION # 182
An organization formally accepts residual risk for a low-impact AI capability after confirming it falls within the enterprise risk tolerance.
What is the primary benefit of this risk acceptance action?
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. When residual risk is within tolerance and affects non-critical functions, formal acceptance is proportionate and allows scarce mitigation resources to be directed toward higher-impact exposures.
Acceptance must still be documented and monitored rather than treated as inaction. This makes option A, It enables the allocation of mitigation resources to more critical AI-specific issues, 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 # 183
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: 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. 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 # 184
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