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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation - AI model and data risk identification | |
| Topic 2: AI Risk Program Management | 42% | - Enterprise AI risk program design - AI risk assessment and treatment strategies - AI risk monitoring and continuous improvement - AI governance communication and reporting |
| Topic 3: AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment |
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NEW QUESTION # 33
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 # 34
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
NEW QUESTION # 35
An organization uses AI to generate procedure documents for operational processes. Which of the following would be of GREATEST concern to a risk practitioner?
Answer: B
Explanation:
AI-generated content-including operational procedures-can contain errors, omissions, hallucinations, and contextually inappropriate guidance. Human review is a critical quality control and accountability mechanism that ensures generated procedures are accurate, complete, and appropriate for actual operational use.
Why A is Correct: The ISACA AAIR guidance on human oversight identifies the absence of human review as the greatest risk in AI-generated documentation. Without review, errors and AI hallucinations are propagated directly into operational use, potentially causing safety incidents, compliance violations, or operational failures. Human review is the last line of defense against AI output quality failures, particularly in operational procedure contexts where incorrect instructions can have serious consequences.
Why B is Wrong: Outdated procedures are a content quality issue that would typically be caught during human review. The greater concern is that no review is occurring, which allows all types of errors-including outdated content-to reach operational use unchallenged.
Why C is Wrong: Policy misalignment is a governance concern but represents a specific type of error that would be identified if adequate human review were performed. The absence of review is the root governance failure.
Why D is Wrong: Using AI to generate procedures for high-risk activities is a deployment scope concern that raises the stakes of errors. However, the fundamental governance failure-and the greatest concern-is that no human verification occurs regardless of the risk level of the activity.
NEW QUESTION # 36
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: A
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 # 37
An organization uses multiple external data sources to train its AI models. Which of the following is the risk practitioner's BEST recommendation to protect the organization from data poisoning attacks?
Answer: C
Explanation:
Data poisoning attacks involve malicious modification of training data to degrade model performance or introduce backdoors. With multiple external data sources, the attack surface for introducing poisoned data is broad and requires proactive, continuous detection at the ingestion stage.
Why B is Correct: The ISACA AAIR adversarial AI guidance identifies continuous monitoring and anomaly detection at the data ingestion pipeline as the most effective defense against data poisoning. By monitoring incoming data in real time for statistical anomalies, unexpected distributions, or known poisoning patterns, organizations can detect and block malicious data before it contaminates training datasets. This preventive approach is superior to reactive detection after poisoning has occurred.
Why A is Wrong: Reactive data integrity reviews triggered by model drift occur after poisoning has already affected model behavior. By this stage, the model may have been deployed and made harmful decisions.
Prevention during ingestion is superior to post-drift investigation.
Why C is Wrong: Model code and deployment artifact controls address security of the software pipeline but do not protect training data from external poisoning. Data integrity requires data-layer controls, not code security.
Why D is Wrong: Regularization reduces overfitting to training noise but does not detect or prevent deliberate poisoning attacks. A sufficiently targeted poisoning attack can introduce systematic bias that regularization techniques cannot mitigate.
NEW QUESTION # 38
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