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.
[References: AI Security Management™ (AAISM) Body of Knowledge — Adversarial ML Threats and Training-Time Attacks; Secure Data Ingestion and Validation Controls. AAISM Study Guide — Poisoning Prevention: Provenance, Validation, and Sanitization Gates., ]
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