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| Section | Objectives |
|---|---|
| Topic 1: AI Risk Management | - Risk identification and assessment for AI systems
|
| Topic 2: Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| Topic 3: AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Topic 4: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| Topic 5: AI Lifecycle Controls | - Controls across AI development lifecycle
|
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NEW QUESTION # 37
Which of the following is the MOST important consideration to reduce risk during the development of a large language model (LLM)?
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. Security should be assessed throughout LLM design, development, testing, deployment, and maintenance so vulnerabilities and unsafe behaviors are identified before becoming embedded in production. Threat analysis and staff training are supporting activities within that broader secure lifecycle. This makes option D, Ensuring security is assessed throughout the development life cycle, 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 # 38
A risk practitioner assesses a new AI system and determines that the risk is within the organization's risk tolerance. Which of the following is the BEST recommendation to ensure system controls remain effective over time?
Answer: D
Explanation:
Even when an AI system is initially assessed as within risk tolerance, its risk profile evolves as the system encounters new data, the operational environment changes, and model performance drifts. Controls that were effective at deployment may become insufficient as these changes accumulate.
Why C is Correct: The ISACA AAIR operational monitoring guidance identifies continuous monitoring for data and performance drift as the most important mechanism for maintaining control effectiveness over time.
Drift detection provides early warning when the AI system begins behaving differently from its validated state-enabling timely control adjustments before risk tolerance is breached. This is particularly critical because AI systems can degrade gradually in ways not visible without active monitoring.
Why A is Wrong: Framework alignment establishes the control baseline but does not actively verify that controls remain effective as the system evolves. Frameworks provide structure; monitoring provides assurance.
Why B is Wrong: Security and risk awareness training is an important human capability development activity but does not detect technical changes in AI system behavior. Training does not substitute for technical monitoring.
Why D is Wrong: Periodic compliance reviews occur at scheduled intervals and may miss drift that develops between review cycles. Continuous monitoring provides real-time detection that periodic reviews cannot match.
NEW QUESTION # 39
A social media platform uses AI tools to automate real-time content moderation. Which of the following would be of GREATEST concern to a risk practitioner?
Answer: C
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. Automated content moderation can falsely restrict users, so a meaningful appeal process is essential for contestability, human review, and correction of erroneous decisions. Compute cost and reinforcement learning are not the primary governance concern. This makes option D, Lack of an appeal process for users falsely accused of violating terms of service, 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 # 40
Which of the following is MOST important to verify when assessing open-source AI components in an organization's supply chain?
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. Open-source AI components should be traceable so the organization can verify origin, dependencies, integrity, licensing, and maintenance history. Geography and cost are secondary, and documentation ownership alone does not establish trustworthy provenance. This makes option A, Traceability for open-source AI artifacts, 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 # 41
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 # 42
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