Guide

Enterprise AI Platforms with HIPAA Compliance

For healthcare, insurance, and financial services organizations, the defining AI challenge isn't adoption, it's deploying AI without exposing PHI or violating the controls auditors and regulators expect. The stakes are the steepest in industry: healthcare data breaches average $9.77 million per incident, the costliest sector for 13 consecutive years, and the HHS Office for Civil Rights has levied over $144.8 million across 152 HIPAA enforcement actions in that period. HIPAA compliance isn't a procurement checkbox; it determines whether sensitive data can legally flow through an AI system at all.

Key takeaways

Any platform processing PHI must execute a Business Associate Agreement (BAA) and demonstrate sustained security controls before PHI flows
Certifications validate past controls; runtime enforcement determines whether platforms stay compliant as AI scales
Evaluation criteria: BAA availability, data residency controls, deployment architecture, and automated evidence generation for auditors
Five platforms compared: Trussed AI, Microsoft Azure OpenAI Service, Google Cloud Vertex AI, AWS Bedrock, and Anthropic Claude Enterprise

What makes an AI platform truly HIPAA compliant?

Three layers. Legal: a BAA covering the AI service, without it, sending PHI is a violation regardless of technical controls. Attestation: sustained-controls evidence, Type II reports (controls verified operating over 3 to 12 months) rather than Type I point-in-time design validation; procurement teams in regulated industries overwhelmingly require Type II. Operational: the controls that keep you compliant as usage scales, prompt-level PHI protection, region-controlled processing, retention and deletion controls, private deployment options, and automated audit evidence. A platform can hold a BAA and certifications and still expose PHI if prompts are logged by default on shared infrastructure, which is why runtime enforcement is the differentiator.

The five platforms

Trussed AI, an enterprise AI control plane built for regulated industries; functions as a drop-in proxy enforcing compliance at runtime with no application code changes. Turns static policy into enforcement: blocking non-compliant outputs, PHI detection and redaction in the request path, model routing, and audit-ready evidence generated from every governed interaction. Best fit: organizations that need their HIPAA controls enforced across whichever models and apps they use.
Microsoft Azure OpenAI Service, OpenAI models hosted in Azure's infrastructure with BAA coverage, regional deployment, and enterprise identity integration. Strong within the Azure perimeter; governance of usage remains the customer's job.
Google Cloud Vertex AI, broad model garden with BAA support, VPC service controls, and data residency options; same caveat on usage-level governance.
AWS Bedrock, multi-model access under AWS's compliance umbrella with private networking; governance primitives, not policy enforcement.
Anthropic Claude Enterprise, enterprise controls and data-handling commitments suited to regulated buyers; pair with enterprise-side enforcement for PHI-level policy.

The pattern: hyperscaler platforms make infrastructure compliant; staying compliant in operation, what users send, what models return, what agents do, requires the runtime layer. Trussed complements any of the model platforms above for exactly that reason.

Questions to ask any vendor

Where is data processed, and can region-specific sovereignty be enforced? How long are prompts and completions retained, and can deletion be demanded? Does the platform support private VPC or on-premise deployment to isolate PHI? Does it generate automated compliance evidence, or will auditors get screenshots?

Frequently Asked Questions

Is a BAA alone enough to send PHI to an AI service? It's the legal floor, not the operational ceiling, minimum-necessary, access controls, and audit obligations still apply to how you use the service.

Can we use a non-BAA model if we redact PHI first? Only if redaction is enforced and verifiable in the request path, policy-based redaction with logging, not developer discipline.

Do we need different platforms for different departments? Often you'll use several model platforms; the sustainable approach is one governance layer applying uniform HIPAA policy across all of them.

Related resources