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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability |
| Topic 2: AI Risk Program Management | 42% | - AI risk monitoring and continuous improvement - Enterprise AI risk program design - AI risk assessment and treatment strategies - AI governance communication and reporting |
| Topic 3: AI Life Cycle Risk Management | - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation - AI model and data risk identification |
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NEW QUESTION # 72
A risk practitioner is concerned that an AI model's responses have become more inaccurate over time, leading to diminished customer trust. Which of the following should the risk practitioner recommend be done FIRST?
Answer: A
Explanation:
Incident response for AI model degradation should follow a structured diagnostic process. Before implementing any corrective action, the scope and nature of the accuracy issues must be understood to ensure the response is appropriate and targeted.
Why D is Correct: According to ISACA AAIR incident response guidance, the first step when AI model accuracy deteriorates is to assess the impact-understanding which specific features are affected, how model outputs have changed, and what the business consequences are. This diagnostic step informs all subsequent decisions about whether to retrain, add validation cycles, or take the system offline. Acting without this assessment may waste resources on inappropriate responses or leave critical issues unaddressed.
Why A is Wrong: Adding validation review cycles is a process change that may be appropriate but cannot be determined without first understanding the nature and scope of the accuracy problem. Reviews address a symptom without diagnosing the cause.
Why B is Wrong: Taking the model offline and backing it up is a drastic operational measure that may be disproportionate to the actual issue. This decision requires understanding the severity and scope of the problem, which requires impact assessment first.
Why C is Wrong: Full model retraining is resource-intensive and may not address the root cause if the problem is not training data staleness. Impact assessment must precede the decision to retrain.
NEW QUESTION # 73
Which of the following is the GREATEST risk when an organization lacks clearly defined accountability mechanisms for AI outputs and decisions?
Answer: A
Explanation:
AI systems make decisions that can affect individuals, organizations, and society. When no individual or function is clearly accountable for those decisions, the organization cannot demonstrate due diligence, remedy harms, or mount a coherent legal defense when challenged.
Why D is Correct: The ISACA AAIR framework identifies legal liability as the greatest organizational risk from absent accountability mechanisms. When AI outputs cause harm-discriminatory lending decisions, unsafe autonomous vehicle actions, inaccurate medical diagnoses-the absence of documented accountability makes it impossible to demonstrate responsible governance to courts, regulators, and affected parties. This creates maximum legal exposure across contract, tort, and regulatory law.
Why A is Wrong: Intellectual property exposure is a significant risk in AI contexts (particularly around training data and model weights) but is not primarily caused by absent accountability mechanisms. IP risk arises from access controls and contractual protections.
Why B is Wrong: Ineffective model training is a technical quality issue. While accountability for model development may influence training quality, ineffective training is not the primary risk from absent accountability for outputs and decisions.
Why C is Wrong: Reduced availability is an operational resilience concern. Accountability gaps do not directly cause availability failures, which are driven by architectural and operational factors.
NEW QUESTION # 74
An organization plans to deploy a generative AI system that processes sensitive personal data across multiple countries with varying privacy laws. Which of the following is the BEST course of action to manage legal and regulatory exposure?
Answer: C
Explanation:
Multi-jurisdictional AI deployment requires jurisdiction-specific compliance strategies because privacy and data protection laws vary significantly across countries. A one-size-fits-all approach frequently fails to meet local requirements, while post-deployment remediation creates legal exposure during the gap period.
Why B is Correct: According to ISACA AAIR guidance, the best approach to multi-jurisdictional compliance is to tailor controls to each relevant statutory framework before deployment and maintain audit trails that demonstrate adherence. This proactive, documented approach reduces legal exposure, satisfies regulatory examination requirements, and enables the organization to demonstrate accountability-a key requirement of frameworks like GDPR.
Why A is Wrong: Post-deployment remediation means the organization is non-compliant during deployment, which creates immediate regulatory exposure. Iterative fixes after harm has occurred are inadequate for protecting individuals or the organization.
Why C is Wrong: Uniform global policies cannot satisfy jurisdictions with conflicting requirements-some laws mandate data residency within borders, making cross-border transfer impossible regardless of encryption strength.
Why D is Wrong: Restricting disclosure of model operations conflicts with transparency requirements embedded in many privacy laws, including GDPR's right to explanation. IP protection cannot override regulatory disclosure obligations.
NEW QUESTION # 75
An electricity provider uses an AI model to forecast and detect outages in power grids. Which of the following is the MOST important consideration to ensure the accuracy of model outputs?
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. Grid-outage prediction depends on current operational conditions, so real-time sensor data is essential to model accuracy.
Access monitoring and geographic processing improve security or resilience, while manual entry cannot provide the same continuous signal. This makes option A, Integration of real-time sensor 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 # 76
Which of the following would be of GREATEST concern to a risk practitioner reviewing the testing and validation of an AI-driven technical support system?
Answer: D
Explanation:
AI-driven technical support systems rely on accurate, current knowledge to resolve user issues. Model drift causes the system to diverge from real-world conditions, producing inaccurate outputs that erode user trust, increase escalations, and potentially cause harm if incorrect technical guidance is followed.
Why A is Correct: According to ISACA AAIR validation guidance, inaccurate outputs from model drift represent the greatest risk in a technical support AI because they directly compromise the system's core function-providing correct technical guidance. Inaccurate outputs lead to unresolved issues, potential system damage from wrong instructions, and reputational harm. Unlike the other options, drift-driven inaccuracy affects every user interaction and cannot be remediated without model updates.
Why B is Correct Context: Infrequent training dataset updates are a contributing cause of model drift and are a serious concern, but they are an input factor rather than the manifest risk itself. The concern is the resulting inaccuracy.
Why C is Wrong: Encryption is a security control for data in storage and transit. While important for confidentiality, it does not affect the accuracy of AI outputs or the system's ability to provide correct technical guidance.
Why D is Wrong: Excessive manual sampling is a testing methodology concern that may reduce testing coverage efficiency. However, it represents a process inefficiency rather than a direct risk to output quality- the model's accuracy is the greater concern.
NEW QUESTION # 77
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