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NEW QUESTION # 107
An organization is evaluating whether to classify a new AI use case as "high-risk" under its internal AI risk framework. Which factor is MOST relevant to this determination?
Answer: A
Explanation:
Risk classification frameworks (including the EU AI Act's approach) generally tier risk based on the potential impact of the AI system's decisions on individuals, not on technical implementation details.
NEW QUESTION # 108
Which of the following is the MOST important risk for an IS auditor to consider when reviewing the adoption of an AI system?
Answer: B
Explanation:
Bias in AI decision-making is one of the most critical risks, particularly when AI influences areas like hiring, lending, or healthcare. The AAIA™ Study Guide highlights the ethical and operational consequences of biased models, which may lead to discrimination, legal liability, and reputational damage.
"Bias in AI outputs can originate from skewed training data or flawed algorithms. Auditors must evaluate whether mitigation techniques, such as bias detection and fairness testing, are in place." While costs (A) and industry maturity (B) are considerations, they do not pose the same systemic ethical risk.
Resistance (D) is a change management issue. C represents the most impactful and widespread risk.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Bias and Fairness in AI"
NEW QUESTION # 109
Which of the following is the GREATEST benefit of using large language models (LLMs) to identify potential control deficiencies?
Answer: C
Explanation:
LLMs excel at processing large amounts of unstructured text (e.g., policies, process narratives, procedures). The GREATEST benefit is their ability to summarize and compare multiple documents to identify inconsistencies (B), which can signal potential control weaknesses or misalignments. This aligns with AAIA's description of AI-enabled audit analytics that support document analysis, narrative comparison, and pattern detection for control assessment.
NEW QUESTION # 110
Which of the following is MOST important to consider when auditing an organization's AI procedures?
Answer: B
Explanation:
The integrity of data fed into AI systems is a critical concern. The AAIA™ Study Guide emphasizes that validation and filtration processes are essential to mitigate the risk of data poisoning-an attack that can manipulate model behavior by injecting malicious inputs.
"Data poisoning represents a major vulnerability in AI pipelines. Effective controls include robust validation, filtration, and monitoring of training data sources. These preventive practices are essential to ensure model reliability and security." While options A, B, and C are important operational and training measures, only D addresses a technical risk that can directly compromise model outputs and trustworthiness.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "AI Data Integrity and Attack Prevention"
NEW QUESTION # 111
An IS auditor uses an internally developed generative AI tool to prepare a status update for audit stakeholders. Which of the following is the auditor's MOST appropriate course of action?
Answer: B
Explanation:
Auditors using AI tools to generate reports or updates must ensure the output is reliable, factually accurate, and complete. As per the AAIATM Study Guide, accountability for audit content remains with the auditor, even when generative AI assists with content creation. Therefore, verifying the completeness and correctness of AI-generated content is the primary responsibility.
"AI-generated audit outputs must be validated against source data and professional standards.
The auditor must critically assess AI contributions and not rely on them without human oversight."
NEW QUESTION # 112
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