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| Section | Objectives |
|---|---|
| Topic 1: AI Risk Management | - Risk identification and assessment for AI systems
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| Topic 2: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 3: AI Lifecycle Controls | - Controls across AI development lifecycle
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| Topic 4: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Topic 5: Regulatory and Compliance Requirements | - Global AI regulatory landscape
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NEW QUESTION # 61
Which of the following testing approaches BEST helps to identify risk in an AI-powered customer support solution?
Answer: A
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. Adversarial testing deliberately supplies malformed, misleading, or hostile inputs to reveal failure modes that ordinary accuracy or load testing may miss. This is especially important for customer-facing AI where unexpected inputs can manipulate or degrade outputs. This makes option A, Using adversarial inputs to determine failure modes, 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 # 62
Which of the following is the MOST important consideration to reduce risk during the development of a large language model (LLM)?
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. 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 # 63
Which of the following is the MOST important reason to include AI-specific risk and compliance clauses in vendor contracts?
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. AI-specific contract clauses should clearly allocate liability and responsibilities for breaches affecting models, data, and services. Baseline frameworks and anonymization duties may be included, but enforceable accountability is the key reason for risk-based contracting. This makes option A, Assignment of liability for breaches impacting models and training data, 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 # 64
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: A
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 # 65
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 # 66
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