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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: AI Risk Program Management | 42% | - AI risk assessment and treatment strategies - Enterprise AI risk program design - AI risk monitoring and continuous improvement - AI governance communication and reporting |
| Topic 3: AI Life Cycle Risk Management | - AI model and data risk identification - AI bias, drift, transparency, and control evaluation - AI development, deployment, and monitoring risks |
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NEW QUESTION # 101
Which of the following is the MOST important reason to include AI-specific risk and compliance clauses in vendor contracts?
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. 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 # 102
An organization is designing an enterprise dashboard to support governance of its AI program. Which of the following is the risk practitioner's BEST recommendation?
Answer: A
Explanation:
An enterprise AI governance dashboard must provide decision-makers with a comprehensive, integrated view of AI program health across all dimensions-risk, performance, compliance, ethics, and operations.
Fragmenting this view or focusing on narrow metrics produces an incomplete governance picture.
Why B is Correct: The ISACA AAIR governance reporting guidance recommends aggregating diverse metrics from all AI life cycle stages as the best approach for an enterprise governance dashboard. This comprehensive aggregation enables decision-makers to see the full AI risk and performance picture-from data quality in training through deployment performance, bias monitoring, security incidents, and compliance status-in a single, actionable view. This unified perspective supports informed enterprise-level governance decisions.
Why A is Wrong: Uptime and availability metrics are operational infrastructure indicators that represent only one dimension of AI governance. Focusing primarily on availability misses critical governance concerns including model fairness, accuracy, bias, and ethical compliance.
Why C is Wrong: Risk heat maps based solely on training variance are narrow technical performance indicators. A governance dashboard requires breadth across risk types and life cycle stages, not depth on one specific technical metric.
Why D is Wrong: Assigning dashboard responsibility exclusively to IT centralizes governance reporting in one function that may lack visibility into business risk, ethical compliance, and strategic alignment dimensions of AI governance. Enterprise dashboards require cross-functional input and ownership.
NEW QUESTION # 103
Which of the following should be the MOST important area of focus during the development of data security risk scenarios specific to AI?
Answer: A
Explanation:
AI systems introduce unique security threat vectors that differ fundamentally from conventional IT security scenarios. Risk scenarios must address AI-specific attacks-model poisoning, adversarial inputs, output manipulation-that conventional security frameworks do not cover.
Why A is Correct: The ISACA AAIR AI security risk scenario guidance focuses on attacks that specifically exploit AI system properties-particularly techniques that maliciously alter AI outputs. These AI-specific attack vectors (adversarial examples, model inversion, prompt injection, output manipulation) represent the most important focus for AI security risk scenario development because they target capabilities unique to AI systems and cannot be addressed by repurposing conventional IT security scenarios.
Why B is Wrong: Business unit readiness documentation is a change management and organizational capability assessment activity. It supports AI adoption planning but does not constitute AI security risk scenario development.
Why C is Wrong: Access policy development is an important security control activity but represents control design rather than risk scenario development. Access policies respond to identified risks; they are not themselves risk scenarios.
Why D is Wrong: Quantum encryption is an emerging cryptographic technology addressing future threats to classical encryption. While relevant for long-term data protection planning, it represents a specialized and forward-looking concern rather than the most important focus for current AI security risk scenarios.
NEW QUESTION # 104
An organization runs AI workloads on shared cloud infrastructure. Which of the following is the MOST significant risk?
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. Shared cloud infrastructure creates confidentiality and integrity exposure for model weights and data. Unauthorized access can disclose proprietary models, sensitive training information, or customer data, making this more significant than cost, latency, or scaling concerns. This makes option B, Unauthorized access to model weights and 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 # 105
Which of the following is the PRIMARY benefit of retraining an AI model with refreshed data?
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. Retraining with refreshed data allows a model to learn current patterns and reduce errors caused by outdated examples or changing real-world relationships. Hyperparameter tuning and stakeholder feedback are separate activities, while privacy deletion is a governance requirement rather than the main performance benefit. This makes option C, Reducing errors and misclassifications by allowing the model to learn from recent examples, 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 # 106
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