Is ChatGPT HIPAA Compliant?

Why the consumer app is off-limits for PHI

The Free or Plus version of ChatGPT is a consumer product. OpenAI's Help Center article on HIPAA-eligible products states that ChatGPT Free, Plus, Pro, Team, and self-serve Business are not eligible for a BAA, and that BAA coverage runs through the API platform and sales-managed Enterprise or Edu accounts instead (OpenAI Help Center, accessed August 2026; the pages are the current published statement at the time of writing). No BAA means no contractual commitment from OpenAI to treat what you type as protected health information, and none of the audit-control logging a compliance program needs. A dictated visit note, a patient name paired with a diagnosis, a scanned lab result: none of that belongs in the consumer interface, regardless of how the response looks or how convenient the workflow feels.

This is not a ChatGPT-specific problem. It is true of any general-purpose AI product that has not signed a BAA with you and does not run inside infrastructure you control. The fix is not "find a different chatbot." The fix is building the safeguard layer around whichever model you use.

There is no such thing as "HIPAA certified"

0 is the number of HIPAA certifications that exist for any software product, including ChatGPT, including ours. HIPAA does not issue a certificate the way PCI-DSS or SOC 2 does. There is no badge to earn and no exam to pass. HIPAA compliance is a program: administrative, physical, and technical safeguards, a signed BAA with every vendor that touches PHI, and documented controls that hold up under audit. A vendor who tells you their product is "HIPAA certified" is either confused or selling you something. Our own security page and FAQs say this directly, because it is the honest answer, not a marketing shortcut. If you want the deeper version of this argument, we walk through what compliant software actually requires in HIPAA compliant software: what it actually requires.

The three safeguards, and what each one means for an AI integration

The HHS HIPAA Security Rule organizes requirements into three categories. Each one maps onto a specific decision you make when you wire a language model into a clinical workflow.

Safeguard category	What HHS requires	What it means for an LLM integration
Administrative	Risk analysis, workforce training, a designated security official	A written policy on what data can reach a model, who can query it, and how incidents get reported
Physical	Facility access control, device and media controls	Infrastructure inside a controlled cloud account or VPC, not a vendor's shared consumer environment
Technical	Access control, encryption, audit controls	Every PHI access logged, encryption in transit and at rest, and a model call that never leaves an authorized boundary

The technical safeguard that trips up most teams building with LLMs is audit controls, specified at 45 CFR 164.312(b). That rule requires hardware, software, or procedural mechanisms that record and examine activity in systems containing PHI. A chat interface with no request log, no per-user access trail, and no record of what was sent to a third-party model fails this requirement before it fails anything else.

What a compliant pattern actually looks like

The safer architecture is not "avoid AI in healthcare." It is putting the model behind an interface you control, inside infrastructure you own, with a contract that covers the data.

Three things have to be true at once:

1. A single interface in front of the model. Your application code talks to one internal service, and that service calls whichever model is under contract. If you need to swap models later, you change one integration point instead of rewriting the product. We describe this pattern in more detail in our FAQs.
2. No training on client data. The model provider's terms need to explicitly exclude your data from training runs, and your BAA needs to say so in writing, not imply it.
3. Deployment inside your own cloud account or VPC. PHI never crosses into infrastructure the vendor controls outside your account boundary. Logs, backups, and audit trails live where you can produce them for an auditor without asking permission.

This is the pattern behind two production systems we have shipped. A compounding-pharmacy platform runs prescription routing, consent, and a seven-year immutable audit log entirely inside the client's own environment. A developmental-dentistry practice network runs an AI clinical layer where a provider dictates a visit and a model drafts a structured SOAP note directly into the chart, while a separate vision model reads radiographs, both running inside a Fastify API of more than 80 endpoints that the practice owns outright. Neither system routes PHI through a consumer chat product. Both sit behind a BAA and inside infrastructure the client controls, which is what makes them auditable instead of merely convenient. We cover the build in detail in how we built a HIPAA-aligned AI scribe.

That is the compliant-by-design alternative to a provider pasting a chart note into ChatGPT and hoping nobody checks. The model still does the work. It just does it inside a boundary that can be logged, audited, and defended.

The short version

ChatGPT's consumer tiers are not BAA-eligible, per OpenAI's own Help Center. Using an OpenAI model for clinical work requires an eligible product, a signed BAA, technical safeguards including audit controls under 45 CFR 164.312(b), and deployment inside infrastructure you control. There is no HIPAA certification, for ChatGPT or for anyone else, only a program of safeguards you can prove. The working alternative already exists in production: a model behind a single interface, no training on client data, running inside the practice's own systems, drafting notes and reading radiographs without PHI ever touching a consumer chatbot.