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HIPAA ChatGPT with Browser Protection

77% of employees share sensitive work information with AI tools at least weekly. Real-time browser PII interception reduces leakage incidents by 94%.

April 20, 20268 minute read
HIPAA ChatGPT complianceclinical AI learningPHI browser protectionmedical education AIreal-time PHI interception

The Clinical AI Problem

Doctors and medical students use ChatGPT and Claude every day. They check drug doses. They look up diagnoses. They review care plans. The tools are useful.

But pasting real patient data into these tools is a HIPAA risk. The text goes to the AI provider's servers. Without a signed Business Associate Agreement (BAA) for that service, the act violates HIPAA. Standard ChatGPT and Claude accounts do not include BAAs for clinical use.

The options are not good. Use the AI with real data and risk a violation. Or strip every note by hand before pasting — a slow step that busy clinicians often skip. Skipping it creates the very breach the process was meant to stop.

Why Manual Review Fails

HIPAA Safe Harbor requires removing 18 types of identifiers. A physician will catch a patient name and a date. But some identifiers are easy to miss.

Geographic sub-identifiers are one example. Age combined with an admission date is another — together they can form a covered identifier pair under HIPAA. These patterns are not obvious under time pressure.

Menlo Security's 2025 research found that real-time browser PHI interception cuts leakage by 94%. That gap shows what clinicians miss versus what tools catch. Cyberhaven data confirms the scale: 77% of employees share sensitive work data with AI tools at least weekly.

How a Browser Extension Helps

A Chrome extension checks text at the moment of submission. It runs before the prompt reaches the AI. The clinician sees a brief preview. It shows what PHI was found and what will be masked.

This is not a hard block. The doctor can proceed, edit, or stop. It adds one short check to an otherwise fast action.

Take an internal medicine teacher using Claude for case-based learning. They paste a case note they already reviewed. The extension runs a second pass. If the note was clean, no alerts appear and the session moves on. If a detail slipped through — a date pair or a small town name — the tool catches it first.

This model fits clinical work well. It keeps the doctor in control. It adds a safety net for the patterns that humans tend to miss.

See our PHI detection accuracy comparison for tool benchmarks. Our HIPAA cloud zero-knowledge guide covers BAA rules and safeguards. The browser DLP guide has setup details.

Sources

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Start anonymizing PII with 285+ entity types across 48 languages.

About this page

We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

Related reading

We follow these rules

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

Our promise

We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

You own your work.

Where we run

Our servers live in Falkenstein, Germany.

We use Hetzner. They hold ISO 27001 certification.

All data stays in the EU.

Backups run every day.

Need help?

Email support@anonym.legal.

We reply within one business day.

How we test

We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

  • We never sell your information to third parties.
  • We never train models on what you upload.
  • We never keep your work after you delete it.
  • We never share keys with any outside firm.
  • We never run ads inside the product.

Plans in plain words

We sell credits, not seats.

One credit covers one short job.

Long jobs use a few credits each.

You can top up at any time.

Unused credits roll over each month.

Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.