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HIPAA-skladen ChatGPT z zascito brskalnika

77 % zaposlenih deli obcutljive delovne informacije z orodji AI vsaj tedensko. Prestrezanje PII v brskalniku v realnem casu zmanjsa incidente uhajanja za 94 %.

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

Klinicna tezava z AI

Zdravniki in studenti medicine vsak dan uporabljajo ChatGPT in Claude. Preverjajo odmерke zdravil. Iscejo diagnoze. Pregledujejo nactre zdravljenja. Orodja so koristna.

Toda lepljenje resnicnih podatkov o pacientih v ta orodja je tveganje za HIPAA. Besedilo gre na streznikov ponudnika AI. Brez podpisanega sporazuma o poslovnem partnerju (BAA) za to storitev dejanje krsi HIPAA. Standardni racuni ChatGPT in Claude ne vkljucujejo BAA za klinicno uporabo.

Moznosti niso dobre. Uporabi AI z resnicnimi podatki in tvegaj krsitev. Ali pa rocno ocisti vsako opombo pred lepljenjem - pocasen korak, ki ga zaposleni kliniki pogosto preskocijo. Preskakovanje ustvari prav tisto krsitev, ki jo je postopek namenjen prepreciti.

Zakaj rocni pregled ne zadostuje

HIPAA Safe Harbor zahteva odstranitev 18 vrst identifikatorjev. Zdravnik bo opazil ime pacienta in datum. Toda nekatere identifikatorje je enostavно spregledati.

Geografski pod-identifikatorji so en primer. Starost v kombinaciji z datumom sprejema je drug primer - skupaj lahko tvorita par pokritih identifikatorjev pod HIPAA. Ti vzorci niso ocitni pod casovnim pritiskom.

Research Menlo Security iz leta 2025 je ugotovil, da prestrezanje PHI v brskalniku v realnem casu zmanjsa uhajanje za 94 %. Ta vrzel kaze, kaj kliniki spregledajo v primerjavi s tem, kar ujamejo orodja. Cyberhaven podatki potrjujejo obseg: 77 % zaposlenih deli obcutljive delovne podatke z orodji AI vsaj tedensko.

Kako pomaga razsiritev brskalnika

Razsiritev za Chrome preveri besedilo v trenutku oddaje. Deluje preden poziv doseze AI. Klinik vidi kratek predogled. Pokaze, kaksna PHI je bila najdena in kaj bo maskirano.

To ni trda blokada. Zdravnik lahko nadaljuje, uredi ali ustavi. Doda en kratek pregled sicer hitremu dejanju.

Vzemimo ucitelja interne medicine, ki uporablja Claude za ucenje na osnovi primerov. Prilepijo opombo o primeru, ki so jo ze pregledali. Razsiritev opravi drugi pregled. Ce je bila opomba cista, ne pridu nobenega opozorila in seja se nadaljuje. Ce je skoznjo zdrsnila podrobnost - par datumov ali ime majhnega mesta - jo orodje najprej ujame.

Ta model se dobro ujema s klinicnim delom. Zdravnik ostane pod nadzorom. Doda varovalno mrеzo za vzorce, ki jih ljudje pogosto spregledajo.

Glejte nas primerjavo tocnosti zaznavanja PHI za primerjalne podatke orodij. Nas vodnik za HIPAA oblak z nicтim znanjem pokriva pravila BAA in varovala. Vodnik za DLP v brskalniku vsebuje podrobnosti nastavitve.

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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.
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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.