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| Section | Weight | Objectives |
|---|---|---|
| AI Auditing Tools and Techniques | 21% | - Audit Planning and Design - Audit Testing and Sampling Methodologies - Audit Evidence Collection Techniques - AI Audit Outputs and Reporting - Data Quality and Analytics for AI Audit |
| AI Governance and Risk | 33% | - AI Models, Considerations, and Requirements - Ethics, Regulations, and Standards for AI - Privacy and Data Governance Programs - AI Governance and Program Management - AI Risk Management |
| AI Operations | 46% | - Third-Party and Supply Chain Risk - Incident Management and Resilience - AI System Lifecycle and Deployment - Operational Controls and Readiness - Performance Monitoring and Evaluation |
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NEW QUESTION # 39
An organization using AI to create digital content faces challenges in protecting its intellectual property. Which of the following is the BEST way to mitigate this risk?
Answer: C
Explanation:
For AI-generated content (images, text, or audio), "watermarking" is a critical technical control to protect Intellectual Property (IP) and ensure provenance. Digital watermarks can be embedded into the metadata or the content itself (often invisibly) to prove the organization is the creator and to track unauthorized distribution. This supports legal claims and brand protection. Firewalls and data separation protect the system, but watermarking protects the output once it is shared online or with clients. As AI makes content creation easier, the ability to prove ownership becomes an essential ethical and legal safeguard.
NEW QUESTION # 40
An organization implements a neural network-based AI system for credit scoring that processes extensive input data from multiple sources. Which of the following represents the GREATEST risk if input validation and anomaly detection controls are inadequate?
Answer: D
Explanation:
In neural network-based credit scoring, the model ' s complexity often obscures the relationship between specific inputs and final outcomes. If input validation and anomaly detection are weak, " biased inputs " or " noise " from various sources can seep into the training and inference pipelines. According to the ISACA AAIA™ framework, these biased inputs can cause the model to learn discriminatory patterns (e.g., correlations between ethnicity and creditworthiness) that are hidden within large datasets. This leads to unethical and potentially illegal credit decisions. While overfitting (Option B) and costs (Option C) are technical concerns, the systemic risk of automated discrimination via unchecked inputs poses the most significant threat to the organization ' s legal standing and reputation.
NEW QUESTION # 41
An organization implements a neural network-based AI system for credit scoring that processes extensive input data from multiple sources. Which of the following represents the GREATEST risk if input validation and anomaly detection controls are inadequate?
Answer: D
Explanation:
In neural network-based credit scoring, the model's complexity often obscures the relationship between specific inputs and final outcomes. If input validation and anomaly detection are weak,
"biased inputs" or "noise" from various sources can seep into the training and inference pipelines.
According to the ISACA AAIATM framework, these biased inputs can cause the model to learn discriminatory patterns (e.g., correlations between ethnicity and creditworthiness) that are hidden within large datasets. This leads to unethical and potentially illegal credit decisions.
NEW QUESTION # 42
An auditor is reviewing consent management for an AI system trained on customer data. Which finding would be the MOST significant concern?
Answer: C
Explanation:
Using data beyond its originally consented purpose (purpose limitation violation) is a significant privacy and regulatory risk, particularly under frameworks like GDPR.
NEW QUESTION # 43
What should be the IS auditor ' s GREATEST concern when an organization is mixing AI-generated content into training data without labeling?
Answer: A
Explanation:
This is known as " Data Contamination " or " Model Autophagy " . When an AI model is trained on data it (or another AI) previously generated, it creates a feedback loop. Errors, hallucinations, and biases in the generated content are re-ingested as " ground truth, " which causes the model to " Collapse " and lose its ability to accurately reflect the real world. The ISACA AAIA™ manual emphasizes that training data must be
" Primary " and " Authentic " to ensure model integrity. Failing to distinguish between human and AI content prevents the organization from detecting and correcting systemic errors in the model ' s logic.
NEW QUESTION # 44
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