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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Program Management | 42% | - AI Risk Monitoring and Reporting - AI Risk Identification and Assessment - AI Risk Response and Mitigation - AI Risk Assurance and Continuous Improvement |
| Topic 2: AI Risk Governance and Framework Integration | 37% | - AI Trustworthiness, Ethical and Societal Implications - AI Policies, Procedures, and Organizational Training - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment - AI Regulatory Compliance and Legal Considerations - AI Models, Frameworks, Strategies, and Use Cases |
| Topic 3: AI Life Cycle Risk Management | 21% | - AI Data and Asset Management - AI Implementation, Maintenance, and Decommissioning - AI Design, Development/Procurement, and Documentation - AI Model Training, Testing, and Validation |
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NEW QUESTION # 90
Which of the following BEST helps to ensure adherence to data minimization principles when using an AI model whose training dataset contains personal information?
Answer: B
Explanation:
Data minimization is a privacy principle requiring that personal data be processed only to the extent necessary for the specified purpose. When training AI models, this means reducing the identifiability of personal data while preserving its statistical utility for model training.
Why D is Correct: According to ISACA AAIR data privacy guidance, pseudonymization directly supports data minimization by replacing identifying attributes with artificial identifiers, allowing the model to train on statistically representative data without processing full personal identifiers. This satisfies minimization requirements under frameworks like GDPR while maintaining training data utility-the specific challenge of AI model development with personal data.
Why A is Wrong: Data Loss Prevention prevents unauthorized transmission of data but does not reduce the amount of personal information contained in training datasets. DLP addresses data exfiltration risk, not data minimization compliance.
Why B is Wrong: Role-based access control restricts who can access the training data but does not reduce the volume or identifiability of personal information in the dataset. RBAC addresses access risk, not data minimization.
Why C is Wrong: Data encryption protects data confidentiality in storage and transit but does not remove or obfuscate personal identifiers from training data. Encrypted personal data is still personal data under privacy law.
NEW QUESTION # 91
An organization is integrating AI systems into core business operations and has decided to establish a formal process to align AI initiatives with corporate values. Which of the following is the GREATEST benefit of this decision?
Answer: A
NEW QUESTION # 92
Which of the following is the PRIMARY benefit of using AI-based data analytic tools to monitor AI system risk?
Answer: B
Explanation:
AI systems generate large volumes of operational data-model outputs, query logs, performance metrics, system telemetry. AI-powered analytics tools can process this data at scale and speed to identify subtle patterns that indicate developing vulnerabilities before they manifest as incidents.
Why B is Correct: According to ISACA AAIR monitoring and analytics guidance, the primary benefit of AI- based risk monitoring tools is their ability to identify latent vulnerabilities through anomaly detection in large datasets. Human analysts cannot process the volume and velocity of data produced by AI systems at sufficient scale to detect subtle, early-stage indicators of emerging risks. AI-powered analytics provide this capability- identifying patterns that precede security incidents, model failures, or compliance violations.
Why A is Wrong: Industry trend forecasting is a strategic risk intelligence activity. While valuable for planning, it represents a secondary, external-facing use of AI analytics rather than the primary benefit of monitoring organizational AI system risks.
Why C is Wrong: Access attempt logging and documentation are security event recording functions. While comprehensive logging is important for audit trails, the primary benefit of AI analytics is pattern detection across that logged data-not the logging activity itself.
Why D is Wrong: Automation of risk analysis and treatment decisions is a contested application of AI in risk management. Human judgment in risk treatment decisions is typically retained as a governance requirement.
Removing human involvement from treatment decisions is not the primary benefit of AI monitoring tools.
NEW QUESTION # 93
Which of the following is the MOST important reason for a risk practitioner to validate the alignment of a deployed AI model with business requirements?
Answer: D
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. A deployed model must remain aligned with the business requirements for which it was approved. Validation helps detect unintended behavior that may be technically plausible but operationally harmful, inappropriate, or outside the intended use case. This makes option A, To mitigate risk from unintended model behavior, 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 # 94
Which of the following is the MOST critical component of a disaster recovery plan (DRP) for data, models, and AI infrastructure?
Answer: B
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. Disaster recovery for AI requires tested restoration processes for production-ready models and supporting artifacts, with version control and remote or segregated storage. Policies and insurance do not restore systems, while broader supply-chain issues belong to continuity planning. This makes option C, Restoration processes for production-ready AI models with version control and remote storage, 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 # 95
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