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HIPAA-uskladjeni ChatGPT sa zastitom pregledaca

77% zaposlenih deli osetljive poslovne informacije sa AI alatima najmanje nedeljno. Presretanje PHI u realnom vremenu u pregledacu smanjuje incidente curenja za 94%.

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

Klinicki AI problem

Lekari i medicinski studenti svakodnevno koriste ChatGPT i Claude. Proveravaju doze lekova. Traze dijagnoze. Pregledaju planove lecenja. Alati su korisni.

Ali lepljenje stvarnih podataka o pacijentima u ove alate predstavlja HIPAA rizik. Tekst odlazi na servere AI provajdera. Bez potpisanog Sporazuma o poslovnom partneru (BAA) za taj servis, ta akcija krsi HIPAA. Standardni ChatGPT i Claude nalozi ne ukljucuju BAA za klinicku upotrebu.

Opcije nisu dobre. Koristite AI sa stvarnim podacima i riskirajte krsenje. Ili rucno uklonite svaku belezku pre lepljenja - spor korak koji zauzeti klinicari cesto preskacuju. Preskakanje stvara upravo proboj koji je taj proces trebalo da spreci.

Zasto rucni pregled ne funkcionise

HIPAA Safe Harbor zahteva uklanjanje 18 vrsta identifikatora. Lekar ce primetiti ime pacijenta i datum. Ali neki identifikatori su laki za propustanje.

Geografski pod-identifikatori su jedan primer. Starost u kombinaciji sa datumom prijema je drugi - zajedno mogu formirati pokriveni identifikatorski par po HIPAA-i. Ovi obrasci nisu ocigledni pod pritiskom vremena.

Menlo Security istrazivanje iz 2025. pokazalo je da presretanje PHI u pregledacu u realnom vremenu smanjuje curenje za 94%. Taj jaz pokazuje sta klinicari propustaju u poredjenju sa tim sta alati hvataju. Cyberhaven podaci potvradjuju obim: 77% zaposlenih deli osetljive radne podatke sa AI alatima najmanje nedeljno.

Kako ekstenzija pregledaca pomaze

Chrome ekstenzija proverava tekst u trenutku slanja. Radi pre nego sto prompt stigne do AI-a. Klinicari vide kratak pregled. Pokazuje koji PHI je pronadjen i sta ce biti maskirano.

Ovo nije tvrda blokada. Lekar moze da nastavi, uredi ili zaustavi. Dodaje jednu kratku proveru inace brzoj akciji.

Zamislite ucitelja interne medicine koji koristi Claude za ucenje zasnovano na slucajevima. Lepi belezku o slucaju koju je vec pregledao. Ekstenzija vrsi drugi pregled. Ako je belezka bila cista, ne pojavljuju se upozorenja i sesija se nastavlja. Ako je neki detalj promasio - par datuma ili naziv malog grada - alat ga hvata prvi.

Ovaj model dobro odgovara klinickom radu. Drzi lekara u kontroli. Dodaje zastitnu mrezu za obrasce koje ljudi imaju tendenciju da propuste.

Pogledajte nas poredjenje tacnosti PHI detekcije za benchmark alata. Nas HIPAA cloud zero-knowledge vodic pokriva BAA pravila i zastitne mere. Vodic za browser DLP ima detalje podesavanja.

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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.