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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting - AI Risk Response and Mitigation - AI Risk Assurance and Continuous Improvement |
| AI Life Cycle Risk Management | 21% | - AI Design, Development/Procurement, and Documentation - AI Model Training, Testing, and Validation - AI Data and Asset Management - AI Implementation, Maintenance, and Decommissioning |
| AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Trustworthiness, Ethical and Societal Implications - AI Models, Frameworks, Strategies, and Use Cases - AI Regulatory Compliance and Legal Considerations - AI Policies, Procedures, and Organizational Training - AI Ownership, Oversight, and Accountability |
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NEW QUESTION # 64
Which AI security by design option BEST mitigates targeted model poisoning and supply chain tampering?
Answer: D
Explanation:
Model poisoning attacks target the training data or model parameters to degrade performance or introduce malicious behavior. Supply chain tampering introduces compromised components at vendor or integration stages. Security by design principles require embedding defenses against these threats from the earliest design stages.
Why C is Correct: According to ISACA AAIR security by design guidance, adversarial resilience and data integrity controls address both model poisoning and supply chain tampering at their root. Adversarial resilience training prepares the model to resist maliciously crafted inputs. Data integrity controls- cryptographic signing, provenance tracking, integrity verification-detect tampering in training data and model artifacts across the supply chain. Together, these form the most comprehensive defense against both attack categories.
Why A is Wrong: Data refreshes with checksums detect post-hoc data corruption but do not build adversarial resilience into the model itself. Checksums verify file integrity but cannot prevent poisoning attacks that maintain file integrity while altering data content.
Why B is Wrong: Frequent retraining and bias monitoring address performance drift and fairness but do not specifically protect against deliberate tampering. A retrained model may still be trained on poisoned data if integrity controls are absent.
Why D is Wrong: Data tokenization protects sensitive field values from unauthorized access (a privacy control) but does not address model poisoning or supply chain tampering, which can occur without accessing or exposing the sensitive field values themselves.
NEW QUESTION # 65
A financial organization is developing an AI model for credit risk assessment. Which of the following is MOST important to ensure the training data supports accurate and unbiased outcomes?
Answer: C
Explanation:
Credit risk assessment AI models trained on unrepresentative datasets perpetuate and amplify historical financial inequities, producing discriminatory outcomes that violate anti-discrimination laws and harm underrepresented borrowers. Dataset diversity is the primary safeguard against training-data-driven bias.
Why A is Correct: According to ISACA AAIR bias and fairness guidance for financial AI, dataset diversity is the most important factor for supporting accurate and unbiased credit risk outcomes. A diverse dataset that represents the full population of potential borrowers-across demographics, income levels, credit histories, and geographies-enables the model to learn genuine risk relationships rather than proxies for protected characteristics. Without diversity, even technically sophisticated models perpetuate discriminatory patterns from historical data.
Why B is Wrong: Supervised learning is a modeling approach, not a data quality characteristic. The choice of supervised learning is appropriate for credit scoring but does not determine whether the training data is representative or unbiased.
Why C is Wrong: Synthetic data augmentation can supplement real data to address specific gaps but cannot substitute for diversity in the underlying real-world data. Synthetic data derived from biased real data may amplify rather than correct the original bias.
Why D is Wrong: Data normalization is a preprocessing technique that scales numerical features to comparable ranges to improve model convergence. It addresses technical modeling quality but has no effect on the representational diversity or demographic fairness of the dataset.
NEW QUESTION # 66
Which of the following is the MOST significant risk when an organization depends on a single AI vendor ' s proprietary environment?
Answer: D
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Dependence on a single proprietary AI environment can create vendor lock-in, making migration technically difficult, operationally disruptive, and expensive. Transparency and scalability are relevant concerns, but prohibitive switching costs are the central dependency risk. This makes option B, Prohibitive migration costs, 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 # 67
Which of the following is the MOST important consideration when selecting controls for AI systems?
Answer: A
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Control selection should start with approved policy, governance, and oversight requirements.
Performance metrics, staff expertise, and integration with existing security processes are important implementation considerations, but controls must first be authorized and aligned with the organization ' s governance framework. This makes option A, Alignment with approved policy and oversight frameworks, 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 # 68
Which of the following BEST mitigates risk associated with evasion attacks on AI models?
Answer: D
Explanation:
Evasion attacks involve adversaries crafting inputs specifically designed to fool AI models into producing incorrect outputs-for example, manipulating images to evade object detection or modifying text to bypass content classifiers. Detecting these attacks requires identifying inputs that are statistically unusual or inconsistent with legitimate use patterns.
Why B is Correct: The ISACA AAIR adversarial AI security guidance identifies anomaly detection as the most effective mitigation for evasion attacks. Anomaly detection systems monitor input distributions, model query patterns, and output characteristics for statistical deviations that indicate adversarial manipulation. By identifying inputs that fall outside expected distributions or trigger unusual model responses, anomaly detection catches evasion attempts before they produce harmful outputs.
Why A is Wrong: API rate limiting controls query frequency to prevent brute-force model probing but does not detect or prevent crafted adversarial inputs sent at normal rates. An attacker can evade rate limits by spacing requests or distributing queries.
Why C is Wrong: Predictive analytics uses historical patterns to forecast future outcomes. It does not specifically detect real-time adversarial manipulation of model inputs.
Why D is Wrong: Feature importance weighting adjusts how much different input features influence model predictions. While it can improve robustness to irrelevant features, it does not detect adversarial inputs specifically crafted to exploit important features.
NEW QUESTION # 69
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