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| Section | Objectives |
|---|---|
| AI Risk Management | - Risk identification and assessment for AI systems
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| AI Lifecycle Controls | - Controls across AI development lifecycle
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| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Regulatory and Compliance Requirements | - Global AI regulatory landscape
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NEW QUESTION # 49
Which of the following would be of GREATEST concern to a risk practitioner reviewing an AI acceptable use policy for an organization that operates in a highly regulated environment?
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. In a highly regulated environment, an acceptable use policy without explicit privacy rules leaves users without clear limits for entering, processing, storing, or sharing regulated information through AI systems. This creates direct compliance and data-protection exposure. This makes option B, Lack of explicit data privacy rules and best practices, 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 # 50
A risk practitioner is performing a post-implementation review for an AI system used for credit scoring.
Which of the following is MOST important for the risk practitioner to confirm?
Answer: A
Explanation:
Credit scoring AI systems make high-stakes financial decisions that directly affect individuals' access to credit. Post-implementation review for such systems must confirm that the system performs within ethical, legal, and regulatory boundaries-particularly regarding fairness and explainability.
Why B is Correct: According to ISACA AAIR post-implementation review guidance for high-stakes AI, confirming explainability and fairness is the most critical review element for credit scoring systems. Anti- discrimination laws (Equal Credit Opportunity Act, Fair Housing Act) require that credit decisions be explainable and not discriminatory. Fairness testing detects whether the system produces disparate outcomes across demographic groups, while explainability ensures individual decisions can be justified if challenged.
Why A is Wrong: Access token logging is a security audit trail mechanism. While important for access governance, it does not address the primary regulatory and ethical obligations of a credit scoring system regarding decision quality and fairness.
Why C is Wrong: Stakeholder communication of performance metrics is a governance reporting activity.
Metric communication does not confirm the system is making fair, explainable decisions-it only reports on performance indicators.
Why D is Wrong: User ease of learning and use is a user experience and adoption concern. System usability does not determine whether credit scoring decisions are accurate, fair, or legally compliant-which are the primary post-implementation concerns.
NEW QUESTION # 51
Which of the following is the GREATEST risk when AI governance fails to incorporate stakeholder perspectives in policy and design decisions?
Answer: D
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. Excluding stakeholder perspectives can produce AI behavior and outputs that do not match the intended business case, affected-user needs, or acceptable values. Governance must incorporate those perspectives so technical success does not become business or societal misalignment. This makes option A, Misalignment of AI behavior and outputs with expected business cases, 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 # 52
Which of the following is the GREATEST risk when an AI system requires a specific safeguard that cannot be put in place because of technical constraints?
Answer: A
Explanation:
When required safeguards cannot be technically implemented, the risk they were designed to mitigate remains unaddressed. This creates a residual exposure gap where the AI system operates with known, unmitigated vulnerabilities-a fundamental risk management failure for the identified threat.
Why A is Correct: The ISACA AAIR risk treatment guidance identifies elevated residual exposure from absent controls as the greatest risk when required safeguards cannot be implemented. Every required safeguard addresses a specific risk exposure. When that safeguard is technically infeasible, the risk it was designed to prevent remains fully present. This unmitigated exposure may exceed the organization's risk tolerance and require escalation to senior management for risk acceptance or alternative treatment decisions.
Why B is Wrong: Training dataset restrictions relate to model development constraints, not directly to the inability to implement a specific runtime safeguard. This is a separate concern that may arise in some technical constraint scenarios but is not the primary risk of an absent safeguard.
Why C is Wrong: User experience degradation is an operational quality concern. Performance impacts from technical constraints are a usability issue rather than a risk exposure representing the greatest organizational concern.
Why D is Wrong: Operational inefficiency and manual process dependencies are resource and process concerns. While relevant to operational cost and effectiveness, they do not represent the primary risk of an unmitigated security or safety exposure from an absent safeguard.
NEW QUESTION # 53
Which of the following is MOST important for a risk practitioner to verify when reviewing the decommissioning process for a customer-facing AI solution?
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.
Decommissioning a customer-facing AI solution must include secure disposal of personally identifiable information that no longer has a valid retention purpose. Encryption and API revocation are useful while assets remain active, but they do not remove end-of-life privacy exposure. This makes option D, Secure disposal of personally identifiable information (PII), 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 # 54
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