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NEW QUESTION # 33
An IS auditor is evaluating a cybersecurity system that uses "agentic AI" for autonomous response. Which of the following is MOST important to consider?
Answer: A
Explanation:
"Agentic AI" possesses the autonomy to perform actions within a system. In a cybersecurity context, an agent might autonomously block an IP or shut down a database if it detects a threat.
The most significant risk is that a "False Positive" could trigger an automated response that causes a massive service disruption. Auditors must validate the existence of "Guardrails,"
"Human-in-the-loop" approval for high-impact actions, and "Kill Switches" to halt the agent if it behaves erratically.
NEW QUESTION # 34
When auditing an AI system, which of the following steps ensures that AI model behavior is aligned with organizational objectives?
Answer: B
Explanation:
Problem framing (option D) is the process of clearly defining the purpose, scope, and desired outcomes of an AI system before it is built or deployed. According to the ISACA AAIA™ Study Guide, "problem framing is the critical first step that aligns model development and subsequent behaviors with the strategic and operational objectives of the organization." If the AI problem is not framed in accordance with organizational goals, even a technically successful AI model could generate outputs that do not support the organization's mission or priorities.
Algorithm debugging, data transformation, and model training are all important phases but rely on the initial problem framing to ensure their efforts are correctly directed.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Project Lifecycle Alignment"
NEW QUESTION # 35
Which of the following is the BEST way to mitigate data poisoning in an AI model?
Answer: A
Explanation:
Data poisoningoccurs when attackers manipulate training data to corrupt model behavior. The most direct mitigation is to implementrobust data validation and integrity checks(option C), including anomaly detection on input distributions, provenance controls, verification of data sources, and safeguards for pipelines feeding the training set. AAIA highlightsthreats and vulnerabilities specific to AIand the importance of controls that protect data integrity in AI Operations.
Option A (relying on third-party providers) does not inherently eliminate poisoning risk; providers themselves may be vulnerable. Option B (increasing data size) can dilute but not reliably remove malicious samples.
Option D (simpler algorithms) might help interpretability but does not directly prevent poisoned data from influencing the model. The most effective, aligned control isrigorous data validationto ensure only trustworthy data enters the training process.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Threats and Vulnerabilities Specific to AI (controls for AI- related threats).
ISACA risk guidance referencing data integrity and poisoning risks in AI pipelines.
NEW QUESTION # 36
An IS auditor is reviewing AI-driven processes that automate financial approvals. Which of the following poses the GREATEST challenge to maintaining separation of duties (SOD)?
Answer: B
Explanation:
Separation of Duties (SOD) is intended to prevent errors or fraud by ensuring no single person (or entity) has control over all stages of a transaction. When AI automates financial approvals, the
"Lack of transparency in the algorithm" creates a challenge because the logic that grants approval is often opaque. If the algorithm functions as a "Black Box," it may consolidate decision- making power that was previously spread across multiple human roles, without sufficient visibility into how those decisions are made. This can lead to unauthorized or erroneous approvals going undetected. Effective AI governance must ensure that automated approvals are subject to transparent audit trails and secondary human reviews to uphold SOD principles.
NEW QUESTION # 37
Which of the following is the MOST important consideration when auditing the data used for training an AI model?
Answer: C
Explanation:
Representativeness ensures that the training data reflects the full spectrum of conditions the AI model will encounter in production. According to the AAIATM Study Guide, models trained on non-representative data are prone to bias, poor generalization, and underperformance in real- world applications.
"Ensuring that training data accurately represents the operational environment is critical for model reliability, fairness, and scalability. Without it, the model may perform well in testing but fail in actual usage."
NEW QUESTION # 38
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