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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | 21% | - AI Design, Development/Procurement, and Documentation - AI Model Training, Testing, and Validation - AI Implementation, Maintenance, and Decommissioning - AI Data and Asset Management |
| Topic 2: AI Risk Governance and Framework Integration | 37% | - AI Regulatory Compliance and Legal Considerations - AI Trustworthiness, Ethical and Societal Implications - AI Policies, Procedures, and Organizational Training - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases |
| Topic 3: AI Risk Program Management | 42% | - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting - AI Risk Assurance and Continuous Improvement - AI Risk Response and Mitigation |
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NEW QUESTION # 25
Risk practitioners use automated tools to generate potential AI risk scenarios. Which of the following represents the GREATEST risk from that approach?
Answer: A
Explanation:
Automated risk scenario generation tools operate based on programmed logic, historical data, and pattern recognition. They may excel at generating scenarios based on known risks and documented processes but struggle to account for complex organizational interdependencies that are not fully captured in their data inputs.
Why D is Correct: The ISACA AAIR risk scenario development guidance identifies the failure to account for process interdependencies as the greatest risk from automated scenario generation. AI systems do not operate in isolation-they are embedded in complex organizational ecosystems where failures cascade through interconnected processes, systems, and stakeholders. Automated tools may miss these interdependencies, producing scenarios that are technically accurate in isolation but miss the most consequential cascade effects.
Why A is Wrong: Complexity in likelihood and impact scoring is a risk quantification challenge that affects scenario prioritization but does not result in missing scenarios entirely. Complex scoring can be managed through additional analytical methods.
Why B is Wrong: Emerging adversarial attack vectors are a potential blind spot for any tool or analyst working from historical data, but this is a known limitation of retrospective approaches that can be supplemented with threat intelligence. It does not represent the distinctive risk of automated scenario generation.
Why C is Wrong: Underestimating model change impacts is a scenario calibration issue that represents a less severe risk than missing entire categories of scenarios arising from unmodeled interdependencies.
NEW QUESTION # 26
Which of the following is the MOST important consideration when managing changes to an AI model in production?
Answer: C
Explanation:
Changes to production AI models-including retraining, parameter updates, and architecture modifications- can alter model behavior in ways that introduce new biases, reduce accuracy, or create regulatory compliance issues. Validation before deploying changes is the most critical safeguard.
Why C is Correct: According to ISACA AAIR change management guidance for AI systems, rigorous validation to assess changes' effects on predictive accuracy and model bias is the most important change management activity. Production AI models make real-world decisions affecting people and business outcomes. Unvalidated changes may degrade performance, introduce discriminatory patterns, or create regulatory violations that are difficult to detect and remediate after deployment.
Why A is Wrong: Allowing operational teams to adjust configuration parameters in real time bypasses change control processes and creates untracked, unvalidated changes to model behavior. This represents a governance risk, not an acceptable change management practice.
Why B is Wrong: Access controls for new model functionalities are a security and authorization concern.
While important for access governance, they do not address the technical risk that model changes may degrade performance or introduce bias.
Why D is Wrong: Expediting production rollouts to minimize downtime prioritizes availability over quality assurance. Rushing changes without adequate validation trades one operational risk (downtime) for a potentially more severe risk (biased or inaccurate outputs affecting critical decisions).
NEW QUESTION # 27
AI tools can BEST help to mitigate supply chain risk by:
Answer: B
Explanation:
Supply chain risk management requires anticipating disruptions before they materialize. AI's most powerful supply chain contribution is its ability to analyze vast datasets-including signals from suppliers, logistics networks, geopolitical indicators, and environmental data-to predict disruptions with accuracy and lead time that human analysts cannot achieve.
Why B is Correct: The ISACA AAIR AI capability guidance identifies predictive disruption identification as the most significant supply chain risk mitigation AI provides. By processing diverse data signals and identifying patterns that precede supply chain failures, AI enables proactive risk management-allowing organizations to pre-position inventory, identify alternative suppliers, or adjust production schedules before disruptions affect operations.
Why A is Wrong: Automating inventory management is an operational efficiency application. While valuable, it manages existing stock levels rather than predicting and preventing supply disruptions.
Automation cannot anticipate future risks not embedded in current inventory patterns.
Why C is Wrong: Historical security control gap identification is a security audit function. Identifying past security weaknesses does not directly mitigate supply chain disruption risks, which may arise from entirely different categories of risk.
Why D is Wrong: Sentiment analysis on supplier reputation provides one qualitative input to supplier risk assessment. While useful for monitoring reputational signals, it captures only a narrow dimension of supply chain risk compared to comprehensive predictive disruption modeling.
NEW QUESTION # 28
Which of the following AI system considerations BEST mitigates risk associated with model drift?
Answer: B
Explanation:
Model drift occurs when the statistical relationship between model inputs and outputs changes over time, causing previously accurate predictions to become less reliable. Regular retraining with updated, relevant data recalibrates the model to current real-world patterns.
Why A is Correct: According to ISACA AAIR model maintenance guidance, regular retraining with new relevant datasets is the most direct mitigation for model drift. By periodically retraining on current data, the model learns the latest patterns and relationships-counteracting the drift that accumulates as real-world conditions diverge from the original training data. This is the standard industry practice for maintaining production AI models in dynamic environments.
Why B is Wrong: Restricting automated data validation to low-risk models creates a governance double standard that leaves high-risk models more vulnerable. If anything, high-risk models require more rigorous automated validation, not less. This approach increases rather than mitigates drift risk for critical applications.
Why C is Wrong: Maintaining existing dataset variance during preprocessing preserves statistical characteristics from a historical snapshot. If drift has occurred in real-world data, deliberately maintaining old variance levels prevents the model from adapting to new conditions.
Why D is Wrong: Role-based access controls protect model parameters and data from unauthorized modification. While important for security, access controls do not address model drift, which is driven by changing real-world conditions rather than unauthorized changes.
NEW QUESTION # 29
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: 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. 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 # 30
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