ML Threat Modeling: Assume Your Data Is Compromised
Threat modeling for ML means assuming your training data is already compromised. This is crucial for services using public or user-supplied datasets. The main footgun is trusting data sources, as data poisoning can silently corrupt your model's behavior.
Threat modeling for machine learning extends traditional security by assuming your training data and its sources are already compromised. It provides a structured way for security engineers and data scientists to collaborate on new, ML-specific risks. This is critical when your product relies on an ML model, especially if it trains on public datasets or user-supplied content.
Read the original → learn.microsoft.com
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