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| Section | Objectives |
|---|---|
| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| AI Lifecycle Controls | - Controls across AI development lifecycle
|
| AI Risk Management | - Risk identification and assessment for AI systems
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| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
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NEW QUESTION # 77
Which of the following is the MOST important consideration when determining mitigation controls for an AI system?
Answer: C
Explanation:
Control selection for AI systems requires balancing the effectiveness and cost of proposed controls against the potential losses or harms the controls are designed to prevent. This cost-benefit analysis ensures resources are allocated proportionately to risk reduction value.
Why C is Correct: The ISACA AAIR control selection guidance identifies the cost-benefit analysis of control effectiveness versus potential business losses as the most important mitigation control determination factor.
Implementing controls that cost more than the risk they mitigate represents inefficient risk management; failing to implement cost-effective controls that prevent large losses represents inadequate risk management.
This proportionality assessment is the foundation of risk-based control selection.
Why A is Wrong: Risk awareness training is an important enabler of effective risk management but is an organizational capability development activity rather than a control selection criterion. Training supports controls but does not determine which controls to implement.
Why B is Wrong: Control performance baselines and compliance reporting requirements are governance and compliance management activities. While necessary for control monitoring, they describe how controls are measured after selection, not how controls are selected in the first place.
Why D is Wrong: Computational complexity is a technical characteristic of the AI system that influences implementation considerations but is not the primary driver of control selection. The most computationally complex system still requires controls proportionate to its risk profile, not its technical architecture.
NEW QUESTION # 78
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 # 79
An organization seeks to implement a new AI system that uses customer information to create targeted product recommendations. Which of the following is the MOST important consideration to ensure the system complies with regulatory requirements?
Answer: A
Explanation:
Privacy and data protection regulations worldwide-including GDPR, CCPA, and sector-specific laws- impose strict requirements on the collection, use, and processing of personal information. Customer data used for AI systems must be obtained through lawful means with appropriate consent for the specific processing purpose.
Why A is Correct: According to ISACA AAIR guidance on regulatory compliance, the legal basis for processing personal data is the foundational requirement. An AI system built on data collected without proper consent or legal authorization exposes the organization to regulatory penalties, reputational damage, and forced shutdown of the system. Consent must be specific to the AI use case, not merely generic data collection consent.
Why B is Wrong: Backup and storage protocols address data security and resilience, which are compliance requirements but secondary to the lawfulness of data collection. Securely storing improperly obtained data does not cure the regulatory violation.
Why C is Wrong: Human review of recommendations is a governance safeguard for accuracy and fairness, not a regulatory compliance requirement for data collection. Many regulations do not require human review of recommendation systems.
Why D is Wrong: Supervised learning is a modeling technique that does not address regulatory compliance regarding data sourcing. The training methodology is irrelevant to whether the underlying data was legally obtained.
NEW QUESTION # 80
A risk practitioner learns that an organization's AI inventory includes separate listings of AI systems, models, and datasets. Which of the following is the risk practitioner's BEST recommendation to improve AI governance?
Answer: B
Explanation:
An AI inventory that lists systems, models, and datasets separately without showing how they relate to each other creates significant governance blind spots. Understanding interdependencies is critical for comprehensive risk assessment and impact analysis.
Why A is Correct: The ISACA AAIR framework emphasizes that AI governance requires understanding how AI components interact. Mapping interdependencies reveals which datasets feed which models, which systems depend on which models, and how failures cascade across the AI ecosystem. Continuous mapping ensures this understanding remains current as the AI landscape evolves, enabling accurate risk assessment, change impact analysis, and incident response.
Why B is Wrong: Training frequency is a useful operational metric but represents a single attribute addition to inventory records. It does not address the fundamental governance gap of disconnected asset listings.
Why C is Wrong: Automating reconciliation improves inventory maintenance efficiency but does not resolve the architectural problem of separate, unlinked asset listings. An automated process applied to siloed data still produces siloed results.
Why D is Wrong: Assigning oversight to a committee addresses governance accountability but does not improve the quality or utility of the inventory itself. Oversight without integrated data still leaves governance gaps.
NEW QUESTION # 81
Which of the following is the GREATEST organizational risk when AI performance alerts are not escalated to decision-makers for review and decisioning?
Answer: C
NEW QUESTION # 82
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