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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Trustworthiness, Ethical and Societal Implications - AI Organizational Processes and Alignment - AI Regulatory Compliance and Legal Considerations - AI Policies, Procedures, and Organizational Training - AI Ownership, Oversight, and Accountability |
| AI Risk Program Management | 42% | - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting - AI Risk Assurance and Continuous Improvement - AI Risk Response and Mitigation |
| AI Life Cycle Risk Management | 21% | - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation - AI Design, Development/Procurement, and Documentation - AI Data and Asset Management |
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NEW QUESTION # 30
An organization has deployed generative AI tools broadly but lacks a consistent method to refresh governance policies and controls. Which of the following is the risk practitioner's BEST recommendation?
Answer: C
Explanation:
Generative AI capabilities and the associated risk landscape evolve rapidly. Governance policies and controls must be refreshed through a structured, regular process rather than reactively or only when compliance requirements change.
Why A is Correct: According to ISACA AAIR, establishing a regular review cadence with codified reassessment procedures is the most robust approach because it creates a systematic, predictable process for keeping governance current. By documenting when and how policies will be reviewed-including triggers for ad hoc review (new deployments, incidents, regulatory changes)-the organization ensures governance never stagnates regardless of external pressures.
Why B is Wrong: Regulatory alignment is an important input to governance refresh but represents a reactive, external-trigger approach. Relying primarily on regulatory signals means governance lags behind organizational AI changes not covered by new regulations.
Why C is Wrong: Centralizing authority in executive and technical leadership creates decision bottlenecks and reduces the operational agility needed to keep pace with rapidly evolving AI deployments. Distributed governance with clear escalation paths is more effective.
Why D is Wrong: Annual reviews are too infrequent for generative AI tools, which may see significant capability changes and risk profile shifts multiple times per year. Annual compliance audits cannot keep governance current in a rapidly evolving AI environment.
NEW QUESTION # 31
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: D
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 # 32
Which of the following is the BEST way for a risk practitioner to communicate rapidly evolving changes to the organization ' s risk profile resulting from the use of generative AI?
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. Rapidly changing generative-AI risk is best communicated through concrete real-world incidents and business impacts that stakeholders can understand. Technical root causes or exhaustive lists can obscure the practical consequences that decision-makers need to evaluate. This makes option B, Provide real-world examples of risk impacts from generative AI incidents and breaches, 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 # 33
Which control is MOST effective to prevent attackers from embedding malicious instructions within user prompts to alter model outputs?
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. Prompt injection is best addressed at runtime by validating incoming content and controlling model outputs. RBAC limits permissions, encryption protects stored data, and static filters may miss context- dependent attacks, while content validation directly addresses malicious instructions. This makes option D, Content validation, 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 # 34
Which of the following is the MOST important consideration when managing changes to an AI model in production?
Answer: D
Explanation:
Changes to production AI models-including retraining, parameter updates, and architecture modifications- can alter model behavior in ways that introduce new biases, reduce accuracy, or create regulatory compliance issues. Validation before deploying changes is the most critical safeguard.
Why C is Correct: According to ISACA AAIR change management guidance for AI systems, rigorous validation to assess changes' effects on predictive accuracy and model bias is the most important change management activity. Production AI models make real-world decisions affecting people and business outcomes. Unvalidated changes may degrade performance, introduce discriminatory patterns, or create regulatory violations that are difficult to detect and remediate after deployment.
Why A is Wrong: Allowing operational teams to adjust configuration parameters in real time bypasses change control processes and creates untracked, unvalidated changes to model behavior. This represents a governance risk, not an acceptable change management practice.
Why B is Wrong: Access controls for new model functionalities are a security and authorization concern.
While important for access governance, they do not address the technical risk that model changes may degrade performance or introduce bias.
Why D is Wrong: Expediting production rollouts to minimize downtime prioritizes availability over quality assurance. Rushing changes without adequate validation trades one operational risk (downtime) for a potentially more severe risk (biased or inaccurate outputs affecting critical decisions).
NEW QUESTION # 35
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