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Excel PII: honderden kolommen anonimiseren

Excel is een van de meest PII-rijke documenttypen in bedrijfsoperaties. Hier is waarom standaard tekstanalyse faalt bij spreadsheets en wat kolom-context-bewuste detectie anders doet.

June 5, 20268 min lezen
Excel GDPRspreadsheet anonymizationXLSX complianceHR datadata minimization

Waarom Excel uw hoogste-risicobestandstype is

Excel-bestanden zijn een van de grootste GDPR-risico's in de meeste bedrijven. Medische dossiers kunnen meer gevoelige data per rij bevatten. Maar spreadsheets stapelen PII snel op — en compliance-teams missen ze vaak.

Drie dingen maken Excel-bestanden moeilijk te beheren.

Volume: Eén XLSX-bestand kan 50.000 rijen en 100 kolommen bevatten. Dat zijn vijf miljoen cellen. Geen handmatige review kan dit bijhouden.

Structuur: PII in Excel staat in twee soorten contexten. Gestructureerde PII staat in gelabelde kolommen: "E-mail", "Telefoon", "Klant-ID". Ongestructureerde PII staat in vrije tekstvelden: opmerkingen, notities, beschrijvingen. Beide vereisen detectie, maar met andere aanpakken.

Verborgen gegevens: Excel-bestanden kunnen verborgen rijen, verborgen kolommen, verborgen werkbladen en metagegevens bevatten. PII in verborgen elementen is nog steeds PII.

Kolom-context-bewuste detectie

Standaard PII-tools behandelen Excel-cellen als losse tekststukken. Ze analyseren elke cel zonder te weten in welke kolom hij staat.

Kolom-context-bewuste detectie gebruikt de kolomnaam om detectie te sturen:

  • Kolom "E-mailadres" → activeer e-maildetectie met hoge betrouwbaarheid
  • Kolom "Telefoonnummer" → activeer telefoonnummerdetectie
  • Kolom "Opmerkingen" → activeer volledig NLP-pipeline voor vrije tekst
  • Kolom "Bedrag" → geen PII-detectie nodig

Dit reduceert valse positieven in numerieke en financiële kolommen terwijl dekking in tekstvelden wordt verbeterd.

Wat anonym.legal doet voor Excel

  • Structurele kolomdetectie: leest kolomkoppen, past entiteitsdetectie aan per kolom
  • Vrije-tekstveldanalyse: volledige NLP op commentaarkolommen
  • Verborgen-elementenscanning: analyseert verborgen werkbladen en rijen
  • Metadataverwijdering: verwijdert PII uit Excel-bestandsmetadata
  • Auditlog per cel: documenteert precies welke cellen werden geanonimiseerd

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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.
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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.