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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Governance and Framework Integration | 37% | - AI Regulatory Compliance and Legal Considerations - AI Trustworthiness, Ethical and Societal Implications - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment - AI Policies, Procedures, and Organizational Training |
| 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 |
| AI Risk Program Management | 42% | - AI Risk Response and Mitigation - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting - AI Risk Assurance and Continuous Improvement |
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NEW QUESTION # 12
Which of the following would be of GREATEST concern to a risk practitioner reviewing the testing and validation of an AI-driven technical support system?
Answer: C
Explanation:
AI-driven technical support systems rely on accurate, current knowledge to resolve user issues. Model drift causes the system to diverge from real-world conditions, producing inaccurate outputs that erode user trust, increase escalations, and potentially cause harm if incorrect technical guidance is followed.
Why A is Correct: According to ISACA AAIR validation guidance, inaccurate outputs from model drift represent the greatest risk in a technical support AI because they directly compromise the system's core function-providing correct technical guidance. Inaccurate outputs lead to unresolved issues, potential system damage from wrong instructions, and reputational harm. Unlike the other options, drift-driven inaccuracy affects every user interaction and cannot be remediated without model updates.
Why B is Correct Context: Infrequent training dataset updates are a contributing cause of model drift and are a serious concern, but they are an input factor rather than the manifest risk itself. The concern is the resulting inaccuracy.
Why C is Wrong: Encryption is a security control for data in storage and transit. While important for confidentiality, it does not affect the accuracy of AI outputs or the system's ability to provide correct technical guidance.
Why D is Wrong: Excessive manual sampling is a testing methodology concern that may reduce testing coverage efficiency. However, it represents a process inefficiency rather than a direct risk to output quality- the model's accuracy is the greater concern.
NEW QUESTION # 13
Which of the following is a risk practitioner's BEST recommendation to establish accountability for AI system outputs and decisions?
Answer: A
Explanation:
Accountability in AI governance requires that specific individuals or roles be clearly designated as responsible for AI system outputs, decisions, and associated risks. Without formal documentation of ownership, accountability gaps emerge.
Why D is Correct: The ISACA AAIR framework emphasizes that accountability must be explicit and documented, with named individuals assigned to own AI outcomes. Formal role assignments create a traceable chain of responsibility that supports auditability, regulatory compliance, and effective escalation when issues arise. Named ownership prevents diffusion of responsibility.
Why A is Wrong: A centralized task force creates collective responsibility, which can dilute individual accountability. Governance bodies support oversight but do not replace individual role ownership for specific outputs.
Why B is Wrong: Continuous monitoring and KPIs are valuable operational controls but represent monitoring mechanisms, not accountability structures. Monitoring detects issues but does not assign responsibility for them.
Why C is Wrong: Resource allocation reviews address investment efficiency rather than accountability for AI decisions and outputs. This is a management activity, not an accountability framework.
NEW QUESTION # 14
An organization plans to deploy a generative AI system that processes sensitive personal data across multiple countries with varying privacy laws. Which of the following is the BEST course of action to manage legal and regulatory exposure?
Answer: A
Explanation:
Multi-jurisdictional AI deployment requires jurisdiction-specific compliance strategies because privacy and data protection laws vary significantly across countries. A one-size-fits-all approach frequently fails to meet local requirements, while post-deployment remediation creates legal exposure during the gap period.
Why B is Correct: According to ISACA AAIR guidance, the best approach to multi-jurisdictional compliance is to tailor controls to each relevant statutory framework before deployment and maintain audit trails that demonstrate adherence. This proactive, documented approach reduces legal exposure, satisfies regulatory examination requirements, and enables the organization to demonstrate accountability-a key requirement of frameworks like GDPR.
Why A is Wrong: Post-deployment remediation means the organization is non-compliant during deployment, which creates immediate regulatory exposure. Iterative fixes after harm has occurred are inadequate for protecting individuals or the organization.
Why C is Wrong: Uniform global policies cannot satisfy jurisdictions with conflicting requirements-some laws mandate data residency within borders, making cross-border transfer impossible regardless of encryption strength.
Why D is Wrong: Restricting disclosure of model operations conflicts with transparency requirements embedded in many privacy laws, including GDPR's right to explanation. IP protection cannot override regulatory disclosure obligations.
NEW QUESTION # 15
A risk practitioner reviews an AI model that ingests diverse external feeds and determines that their reliability is not consistent. Which of the following BEST mitigates this risk?
Answer: B
Explanation:
Inconsistent data reliability from external feeds undermines model accuracy and creates auditability challenges. The solution requires both understanding where data comes from (provenance) and verifying its quality before it enters the model's learning process (stage gate reviews).
Why C is Correct: The ISACA AAIR data quality governance guidance identifies establishing data provenance and implementing stage gate quality reviews as the comprehensive approach to managing inconsistent external data reliability. Provenance tracking records the origin, processing history, and chain of custody of each data source, enabling quality issues to be traced to their source. Stage gate reviews enforce quality standards at defined points in the data pipeline, preventing unreliable data from advancing to model training.
Why A is Wrong: Weighting historical data over recent samples introduces temporal bias and prevents the model from reflecting current real-world conditions-the opposite of what most AI applications require. This trade-off may be appropriate in specific contexts but is not a general mitigation for inconsistent data reliability.
Why B is Wrong: Updating model versions improves model architecture and training processes but does not resolve the underlying external data quality problems. The model update cannot compensate for ingesting unreliable data.
Why D is Wrong: Reducing data source diversity sacrifices the breadth of information that diverse feeds provide, potentially reducing model performance and representativeness. The goal is to ensure consistent quality from diverse sources, not to reduce diversity.
NEW QUESTION # 16
Which of the following is the PRIMARY reason to include contractual requirements for model updates and disclosures from third-party AI suppliers?
Answer: B
Explanation:
Third-party AI suppliers introduce significant risk through model updates, changes in training data, and modifications to system behavior. Contractual disclosure requirements ensure the acquiring organization can maintain active risk oversight despite not controlling the vendor's development processes.
Why B is Correct: The ISACA AAIR framework emphasizes that third-party AI contracts must protect against harms arising from undisclosed changes. When vendors make silent updates to models, the acquiring organization cannot assess new risks before they affect users, decisions, or regulated outcomes. Timely disclosure requirements enable proactive risk detection and mitigation before individuals are harmed.
Why A is Wrong: Availability guarantees are service-level concerns addressed by SLA provisions. While important operationally, they do not address the risk management imperative of understanding what changes have been made to AI models.
Why C is Wrong: Internal trust-building is a change management consideration, not the primary purpose of contractual disclosure requirements. Contracts address risk obligations, not organizational confidence.
Why D is Wrong: Vendor staff access to sensitive datasets is a data access and privacy concern addressed through data processing agreements and access controls, not model update disclosure requirements.
NEW QUESTION # 17
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