Which of the following approaches BEST enables the separation of sensitive and shareable data in an effort to prevent an AI chatbot from inadvertently disclosing confidential information?
Answer(s): C
Siloing enables clear separation of sensitive and shareable data by enforcing strict boundaries between data sets, ensuring that the AI chatbot can only access and use information explicitly designated as safe for disclosure, thereby preventing inadvertent exposure of confidential data.
An organization is implementing an AI-based credit assessment engine using internal and third-party customer data. Which of the following BEST aligns with data management controls for the AI life cycle?
Answer(s): A
Documented procedures for data sourcing, lineage tracking, and quality validation align directly with AI life cycle data management by ensuring data integrity, traceability, and suitability from ingestion through model training, validation, deployment, and ongoing monitoring.
An organization has discovered that employees have started regularly utilizing open-source generative AI without formal guidance. Which of the following should be the CISO's GREATEST concern?
Answer(s): B
Uncontrolled use of open-source generative AI creates a high risk of sensitive or confidential data being unintentionally disclosed through prompts, training interactions, or generated outputs, making data leakage the most critical immediate concern for the CISO.
An AI application development team has been given access to user information and now must format it to be readable by the AI model. During which phase of the data life cycle would this MOST likely occur?
Data preparation is the phase where raw user information is cleaned, transformed, and formatted into a structure that the AI model can process and understand effectively.
Which of the following attack types is the MOST likely cause of model drift?
Data poisoning introduces malicious or corrupted data into the training or retraining process, causing the model to gradually learn altered patterns over time, which leads to unintended changes in model behavior known as model drift.
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