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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | 21% | - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation - AI Data and Asset Management - AI Design, Development/Procurement, and Documentation |
| Topic 2: AI Risk Program Management | 42% | - AI Risk Identification and Assessment - AI Risk Assurance and Continuous Improvement - AI Risk Response and Mitigation - AI Risk Monitoring and Reporting |
| Topic 3: AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Regulatory Compliance and Legal Considerations - AI Policies, Procedures, and Organizational Training - AI Organizational Processes and Alignment - AI Trustworthiness, Ethical and Societal Implications |
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NEW QUESTION # 180
Which of the following is the MOST important consideration when managing changes to an AI model in production?
Answer: A
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 # 181
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: B
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 # 182
Which of the following is the GREATEST benefit of incorporating AI technology for data asset management?
Answer: C
Explanation:
Data asset management for large-scale AI programs involves processing, cataloging, and maintaining vast quantities of structured and unstructured data. AI-powered automation addresses the scalability challenges of manual data management processes.
Why D is Correct: The ISACA AAIR AI capabilities guidance identifies automating data cleaning and metadata tagging as the greatest practical benefit of AI-powered data asset management. Large datasets- often containing millions of records-require consistent preprocessing and cataloging to be usable for AI training and governance. AI automation achieves this at scale, with speed and consistency that manual processes cannot match, improving data quality and discoverability across the organization.
Why A is Wrong: Justifying synthetic data usage is a model development strategy decision, not a data asset management benefit. The justification for synthetic data depends on use case requirements, not AI automation capability.
Why B is Wrong: AI tools can support security monitoring but do not inherently reduce the initial impact of data poisoning or exfiltration attacks. Security outcomes depend on specific defensive AI applications, not general data management automation.
Why C is Wrong: Overfitting identification during model training is a model development monitoring activity. While AI can support training analytics, this is a narrow benefit compared to the broad, scalable data asset management value of automated cleaning and tagging.
NEW QUESTION # 183
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: C
Explanation:
Credit scoring AI systems are subject to anti-discrimination regulations that prohibit using models that produce biased outcomes affecting protected classes. When bias cannot be eliminated through technical means, continuing to operate the system creates ongoing legal violations and harm to affected individuals.
Why B is Correct: According to ISACA AAIR risk treatment guidance and legal compliance obligations, removing a biased credit-scoring system from production is the appropriate response when bias cannot be technically remediated. Continuing to operate a system known to produce discriminatory credit decisions violates anti-discrimination laws (such as the Equal Credit Opportunity Act), exposes the organization to regulatory enforcement, and causes ongoing harm to affected borrowers. Risk avoidance through system withdrawal is the appropriate treatment when the risk cannot be adequately mitigated.
Why A is Wrong: Requesting senior management risk acceptance for confirmed legal violations is inappropriate because organizations cannot accept risks involving known regulatory breaches. Senior management cannot legitimately authorize continued discriminatory lending practices.
Why C is Wrong: Sourcing a replacement system is a necessary future action but takes time to procure, validate, and deploy. In the interim, the biased system should not continue operating. Removing the system from production should precede replacement planning.
Why D is Wrong: Applying compensating controls to generate offsetting biases compounds the discriminatory problem rather than resolving it. Deliberately introducing additional bias-even in the opposite direction-creates an unpredictably biased model that does not produce fair outcomes.
NEW QUESTION # 184
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: C
NEW QUESTION # 185
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