Defenses: Validation, Anomaly Detection, and Provenance
Explore how to defend ML pipelines from poisoning and backdoor attacks by applying data validation, anomaly detection, and provenance controls. Understand how to map controls to pipeline stages to increase attacker costs, detect subtle manipulations, and maintain evidence for forensics. This lesson teaches practical, scalable strategies to secure model retraining when controlling all data input fully is impossible.
You are rebuilding a retraining pipeline for an image-classification API with multiple data sources and periodic model updates, and you cannot fully lock down every source without cutting product coverage. You also cannot manually inspect every image, label, and model artifact, so defenses have to scale with the pipeline.
The design task is to take a concrete attacker model, meaning which inputs the attacker can influence and at what stage, then choose a small set of controls that both reduce risk and leave useful evidence when something goes wrong. No single control solves poisoning or backdoors because attackers can shift tactics, so the goal is to raise attacker cost, shrink the space of successful attacks, and improve visibility for detection and forensics. That becomes easier when you map controls to pipeline stages, since each stage produces different evidence and has different failure modes.
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