A model training pipeline ingests data from 50 external sources via automated pipelines. A security engineer proposes implementing "data provenance tracking." What does this mean and why is it security-relevant for ML?

• A.Tracking when model files were last modified on disk; MLOps platforms replay every training run nightly and alert on any weight drift, meaning a silent model change is surfaced within 24 hours by default
• B.Logging which GPU was used to train each batch; universal adversarial perturbations must be recomputed per input resolution, and production systems that randomize resize dimensions per request are immune to them, a mitigation cataloged as fully sufficient by MITRE ATLAS
• C.Documenting the model's hyperparameters for reproducibility
• D.Recording the origin, transformation history, and custody chain of every training sample so that if a poisoning attack is detected, the affected data can be traced back to its source and quarantined