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Which of the following controls would BEST help to prevent data poisoning in AI models?
Suggested Answer: B
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The most direct preventative control against data poisoning is robust data validation/ingestion gating: provenance checks, schema and constraint validation, anomaly/outlier screening, label consistency tests, and whitelist/blacklist source controls before data reaches training pipelines. Larger datasets (A) don't inherently prevent poisoning; monitoring (C) is detective; updating a foundation model (D) does not address tainted inputs entering the pipeline.
4 comments · newest first
IS
Isabella
38 minutes ago
Regularly updating the foundational model sounds good, but I wonder if it really prevents data poisoning specifically.
DA
Darell
5 days ago
Increasing the size of the training data set seems like it could help, but I feel like it might not directly address data poisoning.
GE
Gene
10 days ago
I remember a practice question that mentioned continuous monitoring as a way to catch issues early. Could that be relevant here?
ER
Ernestine
16 days ago
I think implementing a strict data validation mechanism might be the best option, but I'm not entirely sure.