anonym.legal

Hoe anonym.legal Werkt

Deterministische, regex-gebaseerde PII-detectie die 100% reproduceerbare resultaten levert. Zelfde invoer, zelfde uitvoer—elke keer. Geen AI, geen gokken, alleen transparante patroonmatching.

Probeer Het GratisTechnische Documentatie

How Does PII Detection Work?

PII detection identifies personal data in text using pattern matching and machine learning. anonym.legal uses a hybrid approach:

  1. 1
    Pattern Matching: Regex patterns detect structured data (SSNs, credit cards, IBANs) with checksum validation.
  2. 2
    Named Entity Recognition: NER models identify names, locations, and organizations in 48 languages.
  3. 3
    Context Scoring: Each detection is scored based on surrounding context to minimize false positives.

This hybrid approach detects 285+ entity types while maintaining deterministic, reproducible results — essential for compliance and legal discovery.

Waarom Regex, Geen AI?

Onze Aanpak

  • 100% reproduceerbare resultaten
  • Volledig controleerbaar voor naleving
  • Geen trainingsdata vereist
  • Transparante besluitvorming
  • Snelle, voorspelbare prestaties
  • Geen modelafwijking in de tijd

AI/ML Benaderingen

  • Resultaten variëren tussen runs
  • Black box besluitvorming
  • Vereist trainingsdata
  • Moeilijk te auditen
  • Hogere computerkosten
  • Modelafwijking in de tijd
Leer meer over onze technologie

Het 10-Stappen Proces

Van invoer tot uitvoer, hier is precies wat er met uw document gebeurt

1

Invoertekst

Dien uw document in via webinterface, API of Office Add-in

2

Taaldetectie

Systeem identificeert de documenttaal voor optimale verwerking

3

Tokenisatie

Tekst wordt in tokens verdeeld voor patroonmatching

4

Patroonmatching

Regex-patronen scannen op 285+ entiteitstypen

5

Contextanalyse

Omringende tekst verbetert de detectienauwkeurigheid

6

Vertrouwensscore

Elke detectie ontvangt een vertrouwensscore

7

Entiteitclassificatie

Gedeclareerde items worden gecategoriseerd op type

8

Resultaten Controleren

Bekijk alle detecties met posities en scores

9

Toepassen van Anonimisering

Kies uw methode: Vervangen, Redigeren, Hashen, Versleutelen of Maskeren

10

Uitvoer Document

Download uw geanonimiseerde document

Alleen beschikbaar op Pro en Business plannen

MCP Server: Privacy-First AI Integratie

Hoe uw gegevens door de MCP Server stromen om AI-tools veilig te houden

1

AI Tool Verzoek

Uw AI-tool (Cursor, Claude) stuurt een verzoek met PII

2

MCP Server Onderschept

Server analyseert en detecteert alle PII-entiteiten

3

Anonimisering

PII wordt vervangen door tokens of geredigeerd

Safe data only
4

AI Verwerking

AI ontvangt en verwerkt alleen geanonimiseerde gegevens

5

Antwoord Terug

AI-antwoorden komen terug via MCP Server

6
Optional

De-tokenisatie

Optioneel: Originele waarden worden hersteld voor de gebruiker

Voorbeeld uit de Praktijk

Voor (met PII)
Verwerk betaling voor John Doe, e-mail john@example.com, kaart 4532-1111-2222-3333

Wat AI ziet

Na (geanonimiseerd)
Verwerk betaling voor PII_PERSON_001, e-mail PII_EMAIL_001, kaart PII_CREDIT_CARD_001

Wat u terugkrijgt

AI ziet nooit uw echte PII
Omkeerbaar met tokenisatie modus
Zelfde token kosten als webapp
Werkt met meerdere AI-tools
Beveiliging van ondernemingsniveau
Leer meer over MCP Server →

Verken Verder

Technologie

Diepgaande analyse van regex-gebaseerde detectie en waarom het beter is voor naleving

Architectuur

Systeemarchitectuur en hoe componenten samenwerken

Beveiliging

Vijf beveiligingslagen die uw gegevens bij elke stap beschermen

Frequently Asked Questions

Why use regex instead of AI for PII detection?

Regex-based detection is deterministic and reproducible. The same input always produces the same output. AI/ML models can be unpredictable and may miss or falsely flag data. For compliance, reproducibility matters.

How accurate is the detection?

Our hybrid approach combines regex patterns with Named Entity Recognition (NER) for high accuracy. All patterns include checksum validation where applicable (credit cards, IBANs, SSNs). False positives are minimized through context-aware scoring.

What happens to my data during processing?

Text is sent to our EU-hosted servers (Hetzner, Germany) over TLS 1.3 for analysis. We don't store your data after processing. With Zero-Knowledge auth, we can't even identify which user made the request.

Can I add custom entity types?

Yes! You can create custom recognizers with your own regex patterns and context words. Custom entities support the same operators (replace, mask, hash, encrypt, redact) as built-in types.

How does reversible encryption work?

The Encrypt operator uses AES-256-GCM encryption with your key. Only you can decrypt. This allows re-identification for audits or legal discovery while keeping data protected in transit and storage.

Zie Het In Actie

Probeer onze PII-detectie en anonimisering gratis met 200 tokens per cyclus.

Maak Gratis Account AanSnelle Startgids

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.