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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Program Management | 42% | - AI Risk Monitoring and Reporting - AI Risk Assurance and Continuous Improvement - AI Risk Response and Mitigation - AI Risk Identification and Assessment |
| Topic 2: AI Life Cycle Risk Management | 21% | - AI Data and Asset Management - AI Design, Development/Procurement, and Documentation - AI Model Training, Testing, and Validation - AI Implementation, Maintenance, and Decommissioning |
| Topic 3: AI Risk Governance and Framework Integration | 37% | - AI Trustworthiness, Ethical and Societal Implications - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment - AI Policies, Procedures, and Organizational Training - AI Regulatory Compliance and Legal Considerations |
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NEW QUESTION # 172
A manufacturing organization has implemented an autonomous navigation system for warehouse operations.
Which of the following should a risk practitioner regard as the MOST significant concern?
Answer: D
Explanation:
Autonomous navigation systems in physical environments like warehouses operate in complex, dynamic spaces where unexpected situations arise regularly. Systems trained on limited scenarios may behave unpredictably-or dangerously-when confronted with conditions outside their training distribution.
Why A is Correct: The ISACA AAIR guidance on autonomous systems identifies the inability to generalize beyond training scenarios as the most significant concern because it creates direct physical safety risks. In a warehouse, an autonomous system that cannot adapt to novel situations-unexpected obstacles, unusual layouts, human workers in unexpected locations-may collide with equipment or personnel, causing injury or property damage. This operational safety risk is the highest priority concern.
Why B is Wrong: Proprietary datasets in the neural network represent an intellectual property and data privacy concern. While relevant, it is a data governance issue that does not create the same magnitude of physical safety risk.
Why C is Wrong: Using AI to accelerate just-in-time processes is an intended operational use. Process acceleration is the value proposition, not a risk concern. The risk lies in how reliably and safely that acceleration is achieved.
Why D is Wrong: Reliance on outside contractors reflects a workforce capability gap but represents a manageable governance risk through appropriate vendor oversight. It does not create the direct physical safety exposure of a system that cannot handle novel situations.
NEW QUESTION # 173
A credit-scoring AI solution exhibits steadily declining accuracy despite unchanged input distributions.
Which of the following should a risk practitioner consider to be the GREATEST risk?
Answer: D
Explanation:
When an AI model's accuracy declines despite stable input distributions, the most likely cause is concept drift-where the underlying relationship between inputs and the target variable changes over time. In credit scoring, this may occur when economic conditions, consumer behavior, or risk patterns shift in ways not captured in the original training data.
Why C is Correct: The ISACA AAIR model drift guidance identifies concept drift as the greatest risk in this scenario because it means the model is making credit decisions based on relationships that no longer hold in the current environment. Faulty credit decisions can lead to incorrect denials of creditworthy applicants, incorrect approvals of high-risk applicants, regulatory violations, financial losses, and harm to individuals- all high-severity consequences for a credit-scoring application.
Why A is Wrong: Technical delays in credit score updates are an operational performance concern. Delays create business friction but do not cause the fundamental accuracy problem described in the scenario.
Why B is Wrong: Underfitting from shortened training cycles is a model development quality issue. The scenario specifies stable input distributions and declining accuracy-characteristic of drift, not underfitting, which would manifest differently.
Why D is Wrong: Increased retraining costs represent a financial efficiency concern. While budgetary impacts are real, they are secondary to the risk of faulty credit decisions affecting individuals and regulatory compliance.
NEW QUESTION # 174
An organization seeks to improve AI oversight by incorporating external perspectives regarding potential public harms from AI use. Which of the following is MOST important to consider?
Answer: C
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. External perspectives are best incorporated through formal procedures for engaging affected communities and independent experts. Metrics and post-deployment complaints are useful, but they do not substitute for direct stakeholder participation when assessing public or societal harms. This makes option A, Formal procedures to solicit and prioritize feedback from affected communities and independent experts, 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 # 175
A risk practitioner assesses an AI model used for predictive diagnostics and finds that the system lacks automated update mechanisms. Which of the following is the GREATEST risk?
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. A predictive diagnostic model that cannot update may continue using assumptions that no longer reflect current conditions, compromising accuracy as the environment changes. Human effort is secondary to the risk of incorrect predictions based on stale relationships. This makes option D, Compromise of predictive accuracy due to outdated model assumptions, 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 # 176
An organization depends on multiple external suppliers for AI models and training datasets. Which of the following is MOST important to have in place in order to reduce supply chain risk?
Answer: D
Explanation:
AI supply chain risk arises when external models or datasets are tampered with, have undisclosed characteristics, or cannot be traced to trusted origins. End-to-end provenance and audit trails address these risks by enabling verification of integrity and origin at every stage of the supply chain.
Why A is Correct: According to ISACA AAIR supply chain risk management guidance, verifiable provenance and audit trails are the most important supply chain protection mechanism. Provenance documentation traces the origin, handling, and transformation history of every externally sourced AI artifact- enabling the organization to verify that models and datasets have not been tampered with, that data sources are legitimate, and that the supply chain has not been compromised. Without provenance, organizations cannot distinguish trustworthy from compromised artifacts.
Why B is Wrong: Indemnity clauses assign financial liability after harm occurs. They provide legal recourse but do not prevent supply chain attacks or help the organization verify artifact integrity before deployment.
Why C is Wrong: Training method documentation provides useful technical context but does not verify that the actual artifacts delivered match the documentation. Documentation can be falsified; provenance verification with cryptographic integrity checks cannot.
Why D is Wrong: A vendor risk manager provides governance oversight and relationship management. While important for managing vendor relationships, a single contact point does not substitute for technical provenance verification of every artifact in the supply chain.
NEW QUESTION # 177
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