HIPAA-Compliant AI Deployment Patterns in Clinical Settings: Privacy-Preserving Techniques and Governance Controls

Abstract

Artificial intelligence (AI) is moving from research prototypes into clinical workflows, yet the deployment of AI systems that process protected health information (PHI) remains constrained by the U.S. Health Insurance Portability and Accountability Act (HIPAA) and by the absence of shared engineering guidance for satisfying it. Existing work tends to treat privacy-preserving machine learning and regulatory governance as separate concerns, leaving practitioners without a concrete mapping from architectural decisions to compliance obligations. This paper contributes a catalog of five reusable deployment patterns for clinical AI that jointly address the HIPAA Privacy and Security Rules: a de-identified analytics zone, cross-entity federated learning, confidential inference within trusted execution environments, a PHI-minimizing retrieval-augmented clinical assistant, and a synthetic data sandbox for development. Each pattern is specified in terms of context, governing forces, structure, and residual risk, and is mapped to the administrative, physical, and technical safeguards of the HIPAA Security Rule as well as to the NIST AI Risk Management Framework and ISO/IEC~42001. We further present a threat model for clinical AI and a decision procedure for selecting patterns based on data sensitivity, trust boundaries, and latency requirements. The goal is to give security engineers, compliance owners, and clinical informaticists a common vocabulary that turns abstract regulatory duties into verifiable architectural controls.