Threat Modeling Machine Learning Pipelines in Hospitals: STRIDE and Beyond
Updated on January 05, 2026 17 minutes read
Frequently Asked Questions
Do I need deep clinical expertise to threat model hospital ML systems?
You don’t need to be a clinician, but you do need to understand where signals come from and how they’re used. Partnering with clinical informaticists helps, especially for timing, units, and workflow impact.
Is STRIDE enough on its own for ML pipelines?
STRIDE gives strong coverage for identity, integrity, auditability, availability, and privilege boundaries. For ML-specific risks like membership inference, model inversion, and supply chain attacks, add OWASP ML Top 10 and MITRE
How does HIPAA relate to threat modeling?
HIPAA Security Rule expectations include protecting confidentiality, integrity, and availability of ePHI and performing risk analysis and risk management. Threat modeling is a practical engineering method for making that risk analysis concrete and testable.
When should I consider differential privacy for hospital ML?
DP is most relevant when model outputs will be widely accessible, published externally, or trained on sensitive or small cohorts. It can reduce certain privacy risks, but it often costs accuracy, so it should be a deliberate risk decision, not a default.
What’s the most common mistake teams make?
They secure the inference endpoint and ignore the rest of the pipeline. In hospitals, ETL drift, feature leakage, overprivileged identities, and artifact promotion gaps are often where the worst failures originate.