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| Section | Objectives |
|---|---|
| Topic 1: AI Risk Management | - Risk identification and assessment for AI systems
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| Topic 2: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Topic 3: AI Lifecycle Controls | - Controls across AI development lifecycle
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| Topic 4: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 5: Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
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NEW QUESTION # 160
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 # 161
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 # 162
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: C
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 # 163
Which of the following should be a risk practitioner's PRIMARY consideration when developing risk scenarios related to adversarial manipulation of a business-critical AI system?
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. Risk scenarios for adversarial manipulation should be classified using criteria aligned with organizational tolerance so likelihood and impact can drive proportionate treatment decisions. Attack prevalence and patch frequency are inputs, not the governing risk criterion. This makes option C, Alignment of risk classification criteria with organizational tolerance, 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 # 164
Which of the following AI capabilities would BEST enable a forecasting system to accurately predict the point at which specific equipment components are likely to fail?
Answer: C
Explanation:
Predictive maintenance for equipment components requires continuous analysis of operational data- vibration, temperature, pressure, electrical signatures-that indicate component health over time. AI systems performing this function must process high-frequency sensor data to detect patterns that precede failure.
Why D is Correct: According to ISACA AAIR AI application guidance, real-time sensor monitoring data analysis is the core capability enabling accurate failure point prediction. By continuously analyzing sensor readings against learned patterns of pre-failure behavior, AI systems can detect early-stage degradation signals and forecast time-to-failure with precision unavailable through periodic inspection or rule-based thresholds.
Why A is Wrong: Root cause identification occurs after a defect has already manifested. For predictive maintenance-predicting failure before it occurs-post-defect analysis provides no forward-looking capability.
Why B is Wrong: Replacement product recommendation is a procurement and inventory support function. It assists in planning responses to predicted failures but is not the capability that enables the prediction itself.
Why C is Wrong: Dynamic inventory management of spare parts supports maintenance operations but is a supply chain function dependent on failure predictions, not a capability that generates those predictions.
NEW QUESTION # 165
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