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Dagelijkse Onbedoelde PII-lekken via ChatGPT

Klantenservice-agents versturen dagelijks onbedoeld klantgegevens naar AI-tools. Hier leest u waarom het technisch onvermijdelijk is zonder onderscheppingslaag en hoe u het oplost.

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

Waarom Lekken Onvermijdelijk Zijn Zonder Technische Controles

Klantenservice-agents gebruiken AI-tools voor drie primaire taken:

  1. E-mailantwoorden formuleren — Plakken van klantbericht als context
  2. Ticketsamenvattingen maken — Kopiëren van ticketinhoud voor AI-samenvatting
  3. Beleid opzoeken — Beschrijven van klantscenario voor AI-advies

In elk van deze gevallen bevat de ingevoerde tekst routinematig klantidentificatoren: namen (uit e-mail handtekeningen), ordernummers (geciteerd in het klantbericht), e-mailadressen (in e-mailheaders), accountnummers (in ticketreferenties).

Dit is geen nalatigheid — het is hoe klantenservice-werk werkt. De klantgegevens zijn het doel van de AI-query.

De Omvang

Een klantenserviceteam van 50 agents die elk 20 AI-assisted tickets per dag verwerken:

  • 1.000 AI-interacties per dag
  • Bij 27,4% gevoelig (Zscaler 2025): ~274 interacties per dag met klant-PII
  • Per jaar: ~100.000 klantgegevens-exposures aan AI-providers

Elk van die exposures is een potentiële GDPR Artikel 32-overtreding.

De Technische Oplossing

Een browser-extensie die als intermediair fungeert tussen de medewerker en de AI-tool:

  • Detecteert PII in tekst vóór indiening
  • Toont een preview van wat wordt geanonimiseerd
  • Vervangt PII door tokens in de werkelijke AI-query
  • Behoudt dezelfde AI-functionaliteit — de agent krijgt nuttige antwoorden

De workflow voor de agent verandert minimaal. De gegevensbescherming is automatisch.

Bekijk de Chrome-extensiepagina en prijspagina voor team-implementatie.

Bronnen

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Klaar om uw gegevens te beschermen?

Begin met het anonimiseren van PII met 285+ entiteitstypen in 48 talen.

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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.