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By · Last updated 2026-04-18

Povratak na blogBezbednost veštačke inteligencije

3,8 dnevnih PII izlaganja u timovima podrske

Svaki agent podrske koji koristi ChatGPT u proseku pravi 3,8 lepljenja osetljivih podataka dnevno. Za tim od 100 osoba, to je 380 GDPR incidenata izlaganja dnevno.

April 18, 20268 min čitanja
accidental PII exposuresupport team ChatGPTCyberhaven 3.8 pastesworkflow PII protectionGDPR daily exposure

Matematika dnevnog PII izlaganja

Cyberhaven istrazivanje pokazalo je da zaposleni u preduzecima u proseku prave 3,8 lepljenja osetljivih podataka u ChatGPT po korisniku dnevno. Za tim podrske od 100 osoba, to je 380 slucajeva korisnickih zapisa koji ulaze u ChatGPT svaki dan.

Svaki slucaj moze biti GDPR krsenje minimizacije podataka po clanu 5(1)(c). Taj clan zahteva da licne informacije budu "adekvatne, relevantne i ogranicene na ono sto je neophodno".

Ovo nisu zaposleni koji ignorisu politiku. Broj 3,8 odrazava normalan rad. Agenti kopiraju korisnicke e-poruke da napisu odgovore. Lepe tekst prituzbe da dobiju empaticne predloge. Ukljucuju detalje naloga da dobiju kontekstualne odgovore. Svako lepljenje je validan korak produktivnosti koji nosi PII sa sobom.

Obuka ponasanja ne resava ovo

EU revizija iz 2024. pokazala je da je 63% ChatGPT korisnickih podataka sadrzalo licno identifikujuce informacije. Samo 22% korisnika znalo je da moze da se odjavi putem podesavanja alata. Vecina sadrzaja zaljepanog u AI asistenta sadrzi PII. Vecina korisnika nije svesna kontrola. Rezultat je svakodnevno izlaganje u velikom obimu.

Obuka politike nailazi na osnovni problem. Navika kopiranja i lepljenja je stara decenijama. Korisnici kopiraju i lepe tekst od prvog dana za racunarom. Prikljucivanje AI alata za chat kao odredista lepljenja dodaje novu destinaciju. Ne menja naviku.

Politika "nemojte lepiti korisnicki PII u AI asistent" trazi od agenata da umetnu korak klasifikacije - "da li ovaj tekst sadrzi PII?" - u naviknu akciju koja nema prirodnu pauzu. Efekti obuke se gube. Kumulativni rezultat 380 dnevnih odluka o lepljenju je rizik uskladjenosti koji politika sama ne moze drzati pod kontrolom.

Gde tehnicke kontrole funkcionisu

Resenje radi na samoj akciji lepljenja. Ekstenzija pregledaca presrece sadrzaj ostave u trenutku kada agent pritisne lepljenje, pre nego sto tekst dodje do polja za unos. Agent vidi modalni prozor za pregled. Pokazuje sta je detektovano i sta ce biti anonimizirano pre slanja teksta.

Ovo nije kontrola blokiranja. Agenti mogu da nastave, prekorisce ili zaustave. To je korak transparentnosti. Dodaje jedan trenutak vidljivosti inace automatskoj akciji.

Zamislite vodju tima podrske nemacke e-commerce kompanije koji pise odgovore na prituzbe kupaca. Tok rada ostaje isti: kopira prituzbu, lepi u ChatGPT, generise odgovor. Ekstenzija dodaje dvosekundnu proveru. Agent vidi da su detektovana imena, adrese i brojevi narudzbi. Agent klikne nastavi. Alat prima anonimizianu verziju. Krsenje uskladjenosti se ne desava.

Nas GDPR vodic za uskladjenost pokriva pravnu osnovu za ove kontrole. Pogledajte takodjeé nas poredjenje AI politike i tehnickih kontrola i vodic za browser DLP za ChatGPT za detalje implementacije.

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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).

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We do not sell your data.

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Our servers live in Falkenstein, Germany.

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Bad runs block the deploy.

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Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

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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.

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

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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.