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NEW QUESTION # 314
An organization deploys an AI-based image recognition system that is vulnerable to evasion attacks. Which of the following approaches BEST helps to ensure the system mitigates these evasion attempts?
Answer: D
Explanation:
Evasion attacks occur when an attacker modifies input data (such as adding subtle noise to an image) to trick a model into misclassification. The AAIATM manual identifies "Adversarial Training" as a primary defense, where the model is intentionally exposed to adversarial examples during the training phase to improve its robustness and resilience. This allows the model to learn the patterns associated with malicious inputs.
NEW QUESTION # 315
An organization deploys an AI recruitment platform to screen job applicants. The IS auditor identifies that the platform's decisions may be influenced by model bias. Which of the following risk mitigation strategies is BEST for the auditor to recommend?
Answer: C
Explanation:
Periodic testing and monitoring for bias is a sustainable, proactive strategy aligned with best practices outlined in the AAIA™ Study Guide. This approach ensures that the AI system remains compliant over time, even as data and hiring conditions change.
"Ongoing fairness assessments help detect emerging biases and ensure that the AI model maintains equitable decision-making standards. Periodic testing also allows organizations to take corrective action before regulatory or reputational damage occurs." Suspending the system (B) or relying solely on external datasets (C) are temporary or limited in scope.
Manual reviews (D) are effective but do not solve the root issue. Therefore, A provides a comprehensive, audit-aligned solution.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Bias Mitigation and Monitoring"
NEW QUESTION # 316
Which of the following is the MOST important step in an AI incident management process to ensure continuous improvement?
Answer: A
Explanation:
Root cause analysis (option B) is the most critical step for continuous improvement because it ensures that incidents are not only resolved but prevented from recurring.
AAIA incident management emphasizes:
* Identifying underlying systemic failures
* Determining whether issues arose from data, model logic, drift, integration, or human factors
* Implementing long-term mitigation strategies
* Updating governance and operational controls
Defining ownership (A) is important early in the process.
Assessing severity (D) prioritizes response.
Archiving logs (C) supports documentation.
But none of these guarantee process learning or long-term resilience.
Root cause analysis is the essential step for organizational maturation and risk reduction.
References:
AAIA Domain 2: Incident Management and Post-Incident Review
AAIA Domain 1: Continuous Improvement in AI Governance
NEW QUESTION # 317
An organization has deployed a generative AI system for customer support that includes frequent updates to the AI model after deployment. Which of the following represents the GREATEST risk?
Answer: D
Explanation:
When AI models are updated frequently in production, continuous monitoring is critical to detect performance degradation, bias drift, hallucinations, and security issues introduced by new versions. A lack of continuous monitoring (option C) means the organization might not promptly detect harmful behaviors or compliance violations, despite frequent changes, exposing it to operational, reputational, and regulatory risk.
NEW QUESTION # 318
Which of the following BEST ensures representativeness in AI systems when assessing training data periodically?
Answer: B
Explanation:
Representativeness means that training data accurately reflects the current real-world environment in which the AI system operates. The BEST way to ensure this is by verifying that the training data remains relevant and aligned with evolving real-world conditions (C). This controls the risk of model degradation, bias, or drift as environments change. AAIA emphasizes continual reassessment of data relevance, freshness, and contextual accuracy.
NEW QUESTION # 319
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