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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - AI Risk Assurance and Continuous Improvement - AI Risk Monitoring and Reporting - AI Risk Identification and Assessment - AI Risk Response and Mitigation |
| AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Policies, Procedures, and Organizational Training - AI Trustworthiness, Ethical and Societal Implications - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Regulatory Compliance and Legal Considerations |
| AI Life Cycle Risk Management | 21% | - AI Model Training, Testing, and Validation - AI Implementation, Maintenance, and Decommissioning - AI Data and Asset Management - AI Design, Development/Procurement, and Documentation |
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NEW QUESTION # 79
A risk practitioner is reviewing an organization's implementation of a business-critical AI decision system.
Which of the following would be of GREATEST concern?
Answer: D
Explanation:
Business-critical AI decision systems require comprehensive testing of failure modes and recovery procedures before deployment. For systems making consequential decisions, untested failure scenarios create significant operational, financial, and reputational risks when failures occur in production.
Why C is Correct: The ISACA AAIR testing and validation guidance identifies insufficient scenario-based failure mode testing as the greatest concern for business-critical AI. Without testing how the system behaves when it fails-what recovery procedures activate, how human oversight is engaged, how data integrity is maintained during failures-organizations cannot be confident the system can be safely operated through failures. For critical systems, untested failure scenarios represent unacceptable operational risk.
Why A is Wrong: Conventional security providers may require AI-specific expertise supplements but represent an operational security management concern rather than the greatest risk to system reliability and safety. Security monitoring can be supplemented without fundamentally threatening critical system operations.
Why B is Wrong: Cross-functional incident training gaps are a significant organizational preparedness concern but represent a human capability gap that can be addressed through training programs. The system design risk of untested failure modes is more fundamental.
Why D is Wrong: Not requiring 100% decision accuracy is appropriate risk tolerance calibration-no AI system achieves perfect accuracy, and setting realistic thresholds is a sign of mature risk governance. This reflects sound risk acceptance practice rather than a governance concern.
NEW QUESTION # 80
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 # 81
An organization integrates multiple AI services using APIs to enhance a customer support chatbot. Which of the following is the GREATEST risk?
Answer: B
Explanation:
API integration with external AI services creates data transmission pathways between the organization and external systems. Customer support contexts involve sensitive personal data-account information, contact details, inquiry content-that may be transmitted through these API connections.
Why B is Correct: The ISACA AAIR security and privacy guidance identifies unauthorized disclosure of sensitive data through insecure API connections as the greatest risk in multi-service AI integration. APIs can be vulnerable to interception, inadequate authentication, or misconfiguration. In a customer support context, exposure of personal data via API vulnerabilities creates privacy violations, regulatory liability, and reputational harm-all more severe than the other listed concerns.
Why A is Wrong: Bias and inaccuracy in chatbot responses are real quality risks but represent service quality issues rather than security or privacy breaches. Inaccurate responses are visible and correctable; data breaches may go undetected.
Why C is Wrong: Customer dissatisfaction from operational delays is a service quality and business risk. It is a manageable consequence of performance issues rather than the greatest risk from API-based AI integration.
Why D is Wrong: Insufficient training datasets affect model quality but are a development concern addressed during the model selection phase. They do not represent the primary operational risk of deploying multi- service API integrations in production.
NEW QUESTION # 82
An organization plans to procure an AI model from a third-party supplier for a critical business function.
Which of the following is MOST important to evaluate during supplier vetting?
Answer: C
Explanation:
AI model procurement for critical business functions requires that the selected model be fit for purpose. An AI model that does not align with the specific use case creates performance, compliance, and risk management failures regardless of its technical sophistication.
Why A is Correct: ISACA AAIR procurement guidance emphasizes use case alignment as the primary vetting criterion. A model optimized for one domain may perform poorly, introduce bias, or generate inaccurate outputs in a different context. For critical business functions, misalignment directly translates to operational risk, decision errors, and potential harm. Use case fit determines whether all other evaluation criteria are even relevant.
Why B is Wrong: Dataset size is a technical characteristic that may indicate breadth of training but does not determine suitability for a specific use case. A large general-purpose dataset may be less relevant than a smaller, domain-specific one.
Why C is Wrong: Industry certifications validate security controls and quality management processes. While useful supplementary evidence, they do not confirm that a model performs appropriately for the organization's specific application.
Why D is Wrong: Emphasis on innovation reflects vendor marketing positioning. For critical business functions, proven suitability and alignment with use cases outweighs novelty or innovation claims.
NEW QUESTION # 83
Which of the following should be the MOST important area of focus during the development of data security risk scenarios specific to AI?
Answer: A
Explanation:
AI systems introduce unique security threat vectors that differ fundamentally from conventional IT security scenarios. Risk scenarios must address AI-specific attacks-model poisoning, adversarial inputs, output manipulation-that conventional security frameworks do not cover.
Why A is Correct: The ISACA AAIR AI security risk scenario guidance focuses on attacks that specifically exploit AI system properties-particularly techniques that maliciously alter AI outputs. These AI-specific attack vectors (adversarial examples, model inversion, prompt injection, output manipulation) represent the most important focus for AI security risk scenario development because they target capabilities unique to AI systems and cannot be addressed by repurposing conventional IT security scenarios.
Why B is Wrong: Business unit readiness documentation is a change management and organizational capability assessment activity. It supports AI adoption planning but does not constitute AI security risk scenario development.
Why C is Wrong: Access policy development is an important security control activity but represents control design rather than risk scenario development. Access policies respond to identified risks; they are not themselves risk scenarios.
Why D is Wrong: Quantum encryption is an emerging cryptographic technology addressing future threats to classical encryption. While relevant for long-term data protection planning, it represents a specialized and forward-looking concern rather than the most important focus for current AI security risk scenarios.
NEW QUESTION # 84
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