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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment |
| AI Life Cycle Risk Management | - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation - AI model and data risk identification | |
| AI Risk Program Management | 42% | - AI risk monitoring and continuous improvement - Enterprise AI risk program design - AI risk assessment and treatment strategies - AI governance communication and reporting |
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NEW QUESTION # 56
An organization uses an AI model that learns from live data streams. Which of the following is the BEST course of action to manage the risk of an adaptive model?
Answer: A
Explanation:
AI models that learn from live data streams continuously update their parameters based on incoming data.
This creates two specific risks: the model's behavior may drift from its validated state as data patterns change (data drift), and adversaries may deliberately introduce malicious data to manipulate the model's learning (data poisoning).
Why D is Correct: According to ISACA AAIR adaptive model risk guidance, implementing automated monitoring for both data drift and data poisoning is the most comprehensive response to live-learning model risks. Automated monitoring operates continuously at the speed of the data stream, detecting statistical changes in input distributions (drift signals) and anomalous data patterns (poisoning signals) in real time- enabling timely intervention before either risk materializes into harmful behavior.
Why A is Wrong: Defense-in-depth for model access controls who can interact with the model but does not address risks arising from the data the model learns from. Access controls are necessary but insufficient for managing adaptive learning risks.
Why B is Wrong: Restricting data sources reduces learning breadth, potentially undermining the model's adaptive capability that creates its value. Periodic inspections are too infrequent for live-learning systems where risks can emerge between inspection cycles.
Why C is Wrong: Dynamic performance thresholds detect output degradation after drift has occurred. While useful as a safety net, this reactive monitoring does not prevent drift or detect poisoning early enough for the live-learning risk context.
NEW QUESTION # 57
After which of the following events is it MOST important to update risk ratings?
Answer: B
Explanation:
Risk ratings must be maintained as current assessments of organizational risk exposure. Events that materially change the risk profile-particularly those indicating active harm or regulatory violations-require immediate risk rating updates to ensure governance responses are calibrated to the current risk reality.
Why A is Correct: According to ISACA AAIR risk monitoring and review guidance, the discovery of discriminatory outputs from an AI system represents a material change in risk exposure that requires immediate risk rating updates. Discriminatory outputs indicate active harm to individuals, regulatory violations, and significant legal and reputational exposure. This event fundamentally changes the risk profile from a potential to an actual harm, requiring escalated risk ratings and treatment responses.
Why B is Wrong: Adding new monitoring metrics improves risk detection capability but does not change the underlying risk levels. New metrics may subsequently detect risks requiring rating updates, but their addition alone is an operational change, not a risk level change.
Why C is Wrong: Vulnerability patch deployment reduces risk by closing specific security gaps, which may lower risk ratings but is less urgent than updating ratings to reflect active harm discovery. Patching is a remediation activity; discriminatory outputs represent ongoing harm requiring immediate escalation.
Why D is Wrong: Creating an oversight committee improves governance capability but does not change the risk profile of AI systems. Governance structure changes affect the organization's ability to manage risk; they do not affect the risk levels themselves.
NEW QUESTION # 58
When identifying AI risk scenarios for a customer service chatbot, which of the following is MOST important for a risk practitioner to evaluate?
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. Risk scenario identification should focus on the severity of potential adverse effects on critical stakeholders and organizational objectives. Technical details such as encryption or dataset volume are controls or implementation factors, while impact drives scenario significance. This makes option C, Severity of adverse effects on critical stakeholders and objectives, 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 # 59
An organization is launching multiple generative AI use cases. The risk practitioner needs to ensure that AI risk decisions align with business objectives and approved risk boundaries of the enterprise.
What is the best action to take?
Answer: A
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. AI risk should be explicitly included in enterprise risk appetite and tolerance so use cases are judged against approved business boundaries. A risk register and data controls are useful, but appetite and tolerance connect treatment decisions directly to business objectives. This makes option D, Include AI in risk appetite and 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 # 60
Which AI security by design option BEST mitigates targeted model poisoning and supply chain tampering?
Answer: C
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 # 61
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