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| Section | Objectives |
|---|---|
| AI Risk Management | - Risk identification and assessment for AI systems
|
| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| AI Lifecycle Controls | - Controls across AI development lifecycle
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
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15. Frage
Which of the following is the BEST approach when using AI-driven security ratings to monitor third-party risk?
Antwort: B
Begründung:
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. Third-party security ratings are useful only when they support business and compliance decisions.
Mapping rating metrics to strategic objectives and obligations turns technical measures into meaningful risk information instead of isolated performance or availability indicators. This makes option C, Map risk metrics to strategic objectives and compliance obligations, 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.
16. Frage
Which of the following is the BEST course of action to mitigate risk during model selection of supervised or unsupervised algorithms?
Antwort: D
Begründung:
Algorithm selection is a foundational risk management decision in AI development. The wrong algorithm for a given use case can produce inaccurate, unreliable, or harmful outputs regardless of the quality of training data or computational resources applied.
Why D is Correct: The ISACA AAIR model development guidance identifies use case alignment as the most critical algorithm selection criterion. Supervised and unsupervised learning are suited to fundamentally different problem types-supervised learning requires labeled training data and learns mappings to known outputs; unsupervised learning discovers patterns in unlabeled data. Selecting algorithms whose capabilities match the use case's structure and objectives prevents systematic performance failures and misapplied AI.
Why A is Wrong: Generalization capability is an important model quality criterion but represents one of many algorithmic properties. Strong generalization on the wrong problem type still produces poor results. Use case alignment precedes generalization as a selection criterion.
Why B is Wrong: Requiring supervised learning for all training projects is an inappropriate blanket policy.
Many valuable use cases-anomaly detection, customer segmentation, exploratory analytics-are better served by unsupervised approaches. Mandating supervised learning prevents optimal use case matching.
Why C is Wrong: Computational cost is a resource management consideration. Optimizing for cost at the expense of use case fit risks deploying inappropriate models that produce unreliable outputs, creating far greater costs through remediation or harm.
17. Frage
Which of the following BEST helps to ensure adherence to data minimization principles when using an AI model whose training dataset contains personal information?
Antwort: B
Begründung:
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.
18. Frage
A risk practitioner discovers that an AI chatbot stores user interactions indefinitely. Which of the following is the MOST appropriate mitigation?
Antwort: C
Begründung:
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. Indefinite chatbot retention creates unnecessary privacy and breach exposure. Automated retention limits linked to documented consent or purpose ensure records are kept only as long as justified while still accommodating legitimate legal, operational, or audit requirements. This makes option B, Automate retention limits linked to documented consent, 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.
19. Frage
Which of the following should be the PRIMARY consideration when determining the priority for restoration of AI systems following a model exfiltration attack?
Antwort: B
Begründung:
Following a model exfiltration attack, multiple AI systems may require restoration. Prioritization must be based on objective criteria that reflect the potential business impact of continued unavailability. Systems supporting critical business functions must be restored before those supporting non-critical functions.
Why A is Correct: According to ISACA AAIR business continuity guidance for AI, the primary criterion for restoration priority is the AI system's criticality to business requirements. Systems that support mission- critical functions-patient care, financial transaction processing, safety operations-represent the highest restoration priority because their unavailability causes the greatest operational harm. This risk-based prioritization framework is consistent with standard business continuity management principles applied to the AI context.
Why B is Wrong: Team member expertise affects restoration capacity and speed but should not drive prioritization decisions. Priority is determined by business impact, not by where the team has the most technical capability. Resource allocation follows priority, not the reverse.
Why C is Wrong: Vulnerability testing and patch costs are operational considerations that may influence restoration timelines but should not override business criticality in determining priority. Cost-based prioritization could lead to restoring cheaper but less critical systems first.
Why D is Wrong: Dataset availability affects the feasibility and timeline of model retraining but is a logistical consideration rather than the primary basis for restoration priority. Critical systems should be prioritized even if their restoration is technically more complex.
20. Frage
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