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| Section | Objectives |
|---|---|
| AI Governance and Risk | |
| AI Auditing Tools and Techniques | |
| AI Operations |
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NEW QUESTION # 109
Which of the following should be an IS auditor's GREATEST concern when using a predictive AI tool to analyze data abnormalities?
Answer: C
Explanation:
For a predictive AI tool analyzing abnormalities, the GREATEST concern is the rate and impact of false positives and false negatives (A). False positives can lead to unnecessary investigation, while false negatives mean true issues (e.g., fraud, control failures) remain undetected. From an assurance perspective, false negatives are especially critical because they directly undermine audit objectives. AAIA underscores that key performance metrics (e.g., precision, recall) and error trade-offs are essential in evaluating AI tools used in audit.
NEW QUESTION # 110
An organization uses an AI image generation platform to create promotional materials. An IS auditor identifies that the platform includes copyrighted images in its training data. Which of the following is the auditor's BEST recommendation to address this issue?
Answer: B
Explanation:
Ensuring that AI tools are trained on properly licensed and documented data sets is critical to avoiding copyright infringement and legal exposure. The AAIA™ Study Guide emphasizes using platforms with certified and traceable training data to meet ethical and legal standards.
"Organizations must verify the provenance and licensing of data used to train AI systems. Platforms that certify data sources reduce the risk of using protected intellectual property without consent." Manual review (A) is resource-intensive and may not detect embedded copyright violations. Labeling (C) is not sufficient for legal protection. Suspension (D) may be excessive without first attempting remediation.
Thus, B is the most strategic and effective recommendation.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Intellectual Property and Data Licensing in AI Systems"
NEW QUESTION # 111
Which of the following is the MOST important consideration for change management related to the organization-wide adoption of AI systems and tools?
Answer: D
Explanation:
Fororganization-wide adoptionof AI, the MOST important change management consideration is that the organization hassuitable data governance and infrastructure readiness(D). Without robust data governance, AI systems may rely on poor-quality, noncompliant, or insecure data; without appropriate infrastructure (compute, storage, monitoring, integration), implementations will be fragile, unreliable, and risky. AAIA stresses thatAI readinessincludes governance structures, data stewardship, and technical foundations.
Senior leadership involvement (A) is critical for sponsorship and culture, but even strong leadership cannot compensate for fundamentally inadequate data governance and infrastructure. Option B (shorter training cycles) focuses on user adoption but not core risk and control issues. Option C (phased implementation and stage gates) is good project discipline, but still depends on the underlying readiness. Therefore, from an AI risk and governance perspective, ensuringdata governance and infrastructure readinessis the primary prerequisite for successful, safe change management in AI adoption.
References:
ISACA,AAIA Exam Content Outline- Domain 1 and Domain 2: Governance of AI; AI Operations and Infrastructure.
ISACA guidance on AI readiness assessments and data governance as a precondition for AI transformation.
NEW QUESTION # 112
Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and integrity for AI model training and deployment?
Answer: A
Explanation:
Assessing data lineage provides insight into the origin, flow, and transformation of data across its lifecycle, which is crucial for validating data governance. The AAIATM Study Guide states that data lineage is essential to ensure the accuracy, consistency, and trustworthiness of data used in training AI models.
"Traceability of data sources is a core tenet of effective data governance. Data lineage validation ensures data quality, prevents unauthorized modifications, and maintains auditability."
NEW QUESTION # 113
An organization is developing an AI system that integrates data from multiple external sources without clearly defined data ownership policies. Which of the following is the GREATEST concern in this situation?
Answer: D
Explanation:
When integrating data from multiple external sources, uncleardata ownershipdirectly affects accountability for privacy, consent, retention, and lawful processing. This createsgaps in AI privacy compliance and accountability(option D), which is the greatest concern because violations can lead to regulatory sanctions, litigation, and serious reputational damage. AAIA's governance and risk domain emphasizesprivacy and data governance programs, including clear roles, responsibilities, and ownership.
Option A is important but relates more to accuracy and performance validation, not the foundational legal and accountability issues. Option B (access permissions) is a control issue but usually easier to remediate once ownership and responsibilities are clarified. Option C (dependence on automated data collection/cleansing) can introduce quality risks but does not directly resolve or define who is accountable. Thus, the primary risk is the lack of clear privacy and accountability structures across external data sources.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Privacy and Data Governance Programs (data governance, privacy considerations).
ISACA guidance on AI governance, roles, responsibilities, and accountability for data use.
NEW QUESTION # 114
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