Securing U.S. Healthcare Infrastructure with Machine Learning: Protecting Patient Data as a National Security Priority

Abstract

U.S. healthcare is a designated critical infrastructure whose disruption jeopardizes public health and national security. Yet escalating cyber risk, driven by large‑scale data breaches and ransomware, has outpaced traditional controls. This paper argues that machine learning (ML) can materially strengthen healthcare cyber defense if it is engineered with security and privacy as first‑class requirements. We synthesize pre‑July‑2022 literature across adversarial ML, privacy‑preserving learning, and medical informatics, and propose an integrated architecture that combines federated learning, secure aggregation, and differential privacy to enable cross‑institutional detection while minimizing data exposure. We map ML techniques to concrete healthcare threat vectors insider misuse of electronic health record (EHR) data, credential‑stuffing against patient portals, lateral movement across medical IoT/telehealth ecosystems, and tampering with AI‑enabled clinical decision support and outline controls that align with NIST SP 800‑53 and Zero Trust. A methodology section details data sources (EHR access logs, identity and access management telemetry, endpoint/IoMT signals, and clinical text), model families (unsupervised anomaly detection, sequence and graph models, and privacy‑preserving pipelines), governance (threat modeling, red‑teaming, privacy budgets, and model risk management), and evaluation (detection efficacy, time‑to‑detect, and formal privacy loss). We further discuss adversarial risks unique to medicine and the policy implications of deploying ML in regulated environments governed by HIPAA and FDA device guidance. Two figures visualize breach trends and cost asymmetries; tables operationalize the control mapping and measurement plan. We conclude that secure ML is not a panacea, but a necessary capability for resilient care delivery. Properly engineered, it can reduce dwell time, contain blast radius, and enable sector‑wide learning without centralized PHI pooling advancing both patient privacy and national security.