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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Program Management | 42% | - Enterprise AI risk program design - AI governance communication and reporting - AI risk monitoring and continuous improvement - AI risk assessment and treatment strategies |
| Topic 2: AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases |
| Topic 3: AI Life Cycle Risk Management | - AI model and data risk identification - AI bias, drift, transparency, and control evaluation - AI development, deployment, and monitoring risks |
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NEW QUESTION # 158
Which of the following information is MOST important to add to an organizational business continuity plan (BCP) when adopting a customer-facing AI solution?
Answer: B
Explanation:
Business continuity planning for customer-facing AI solutions must ensure service availability and resilience under failure conditions. The BCP must specify the technical and operational mechanisms that maintain service continuity when primary systems are disrupted.
Why B is Correct: The ISACA AAIR business continuity guidance identifies secure access to alternate resources, multi-region failover, and load balancing as the most important additions to a BCP for customer- facing AI. These mechanisms ensure that service disruptions-whether from technical failures, cyber incidents, or regional outages-do not result in total unavailability. For customer-facing solutions, maintaining service continuity directly affects customer trust, revenue, and regulatory compliance with service availability obligations.
Why A is Wrong: Post-incident audits of recovery times and accuracy metrics are monitoring activities that occur after incidents. While valuable for improvement planning, they do not define the recovery mechanisms that the BCP must specify to ensure continuity during disruptions.
Why C is Wrong: Centralizing failover under a single cloud provider creates a concentration risk-if that provider experiences an outage, all failover mechanisms fail simultaneously. Good BCP design requires geographic and provider diversification, not concentration.
Why D is Wrong: Breach containment criteria address security incident response, not service continuity.
While related to incident management, breach response procedures are typically documented in the incident response plan rather than the BCP, which focuses on maintaining or restoring business operations.
NEW QUESTION # 159
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: D
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 # 160
A risk practitioner learns that an AI system used by a manufacturer for quality control (QC) has produced inaccurate responses that could potentially impact user safety. Which of the following is the risk practitioner's BEST recommendation to mitigate this risk?
Answer: C
Explanation:
When an AI system used for safety-critical quality control produces inaccurate responses that could harm users, the most immediate and effective safeguard is inserting human judgment into the decision process before unsafe outputs can reach production or end users.
Why A is Correct: The ISACA AAIR human oversight guidance identifies human-in-the-loop reviews as the most effective mitigation when AI outputs pose safety risks. In safety-critical applications like manufacturing quality control, human reviewers can catch and correct AI errors before they result in unsafe products reaching consumers. This control is immediately implementable, does not require model retraining, and directly addresses the safety risk. It is the appropriate response when AI accuracy cannot be fully trusted.
Why B is Wrong: Bias and fairness testing is a model evaluation activity that assesses whether outputs are systematically skewed. While useful for improving the model, it does not provide immediate protection against the safety risk of current inaccurate outputs.
Why C is Wrong: Synthetic data augmentation may improve model quality over time but requires model retraining and does not prevent currently inaccurate outputs from causing harm in the interim.
Why D is Wrong: Prompt engineering training improves how users interact with AI systems to elicit better outputs. It is useful for generative AI applications but does not directly address safety risks from QC system inaccuracies, which require operational oversight rather than improved prompting.
NEW QUESTION # 161
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: B
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 # 162
An election oversight body is considering the use of AI to identify irregularities in voting patterns. Which of the following is the MOST important risk to evaluate?
Answer: B
Explanation:
AI systems trained on historical data inherit the biases, patterns, and structural inequities embedded in that data. In electoral contexts, historical voting patterns may reflect systemic disenfranchisement, gerrymandering, or demographic manipulation-biases that an AI system could amplify and legitimize through its outputs.
Why B is Correct: According to ISACA AAIR bias and fairness guidance applied to high-stakes public sector AI, the amplification of historical data biases poses the greatest risk in electoral irregularity detection. If the AI system treats historically suppressed voting patterns as the normal baseline, it may flag legitimate turnout increases in previously underrepresented communities as irregularities-producing discriminatory, biased outputs with severe democratic consequences.
Why A is Wrong: Voter location identification is a privacy concern but represents a specific data element risk.
Comprehensive privacy controls can mitigate location exposure without resolving the systemic bias risk.
Why C is Wrong: Contextual drift-the model performing differently in new electoral contexts than in training contexts-is a technical risk that is relevant but addressable through validation testing. Bias amplification is a more fundamental concern embedded in the historical data itself.
Why D is Wrong: Political distrust of AI represents a stakeholder acceptance challenge. While significant for implementation success, it is a communication and change management concern rather than the primary technical and ethical risk from the AI system itself.
NEW QUESTION # 163
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