anonym.legal

Hvordan anonym.legal Fungerer

Deterministisk, regex-baseret PII detektion der leverer 100% reproducerbare resultater. Samme input, samme output—hver gang. Ingen AI, ingen gætteri, kun gennemsigtig mønstergenkendelse.

Prøv Det GratisTeknisk Dokumentation

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.

Hvorfor Regex, Ikke AI?

Vores Tilgang

  • 100% reproducerbare resultater
  • Fuldstændig reviderbar for overholdelse
  • Ingen træningsdata krævet
  • Gennemsigtig beslutningstagning
  • Hurtig, forudsigelig ydeevne
  • Ingen modelafvigelse over tid

AI/ML Tilgange

  • Resultater varierer mellem kørsel
  • Black box beslutningstagning
  • Kræver træningsdata
  • Svært at revidere
  • Højere beregningsomkostninger
  • Modelafvigelse over tid
Lær mere om vores teknologi

Den 10-Trins Proces

Fra input til output, her er præcist hvad der sker med dit dokument

1

Input Tekst

Indsend dit dokument via webinterface, API, eller Office Add-in

2

Sprogdetektion

Systemet identificerer dokumentets sprog for optimal behandling

3

Tokenisering

Teksten opdeles i tokens til mønstergenkendelse

4

Mønstergenkendelse

Regex-mønstre scanner for 285+ enhedstyper

5

Kontekstanalyse

Omgivende tekst forbedrer detektionsnøjagtigheden

6

Tillidsscore

Hver detektion modtager en tillidsscore

7

Enhedsklassifikation

Detekterede elementer kategoriseres efter type

8

Gennemgå Resultater

Se alle detektioner med positioner og scorer

9

Anvend Anonymisering

Vælg din metode: Erstat, Rediger, Hash, Krypter, eller Masker

10

Output Dokument

Download dit anonymiserede dokument

Kun tilgængelig på Pro og Business planer

MCP Server: Privacy-First AI Integration

Hvordan dine data flyder gennem MCP Serveren for at holde AI værktøjer sikre

1

AI Værktøj Anmodning

Dit AI værktøj (Cursor, Claude) sender en anmodning indeholdende PII

2

MCP Server Opsnapper

Serveren analyserer og detekterer alle PII enheder

3

Anonymisering

PII erstattes med tokens eller redigeres

Safe data only
4

AI Behandling

AI modtager og behandler kun anonymiserede data

5

Respons Retur

AI respons kommer tilbage gennem MCP Serveren

6
Optional

De-tokenisering

Valgfrit: Originale værdier gendannes for brugeren

Virkeligt Eksempel

Før (med PII)
Behandl betaling for John Doe, e-mail john@example.com, kort 4532-1111-2222-3333

Hvad AI ser

Efter (anonymiseret)
Behandl betaling for PII_PERSON_001, e-mail PII_EMAIL_001, kort PII_CREDIT_CARD_001

Hvad du får tilbage

AI ser aldrig din rigtige PII
Reversibel med tokeniseringsmetode
Samme tokenomkostninger som webapp
Fungerer med flere AI værktøjer
Virksomhedskvalitet sikkerhed
Lær mere om MCP Server →

Udforsk Yderligere

Teknologi

Dyk ned i regex-baseret detektion og hvorfor det er bedre for overholdelse

Arkitektur

Systemarkitektur og hvordan komponenter arbejder sammen

Sikkerhed

Fem sikkerhedslag der beskytter dine data på hvert trin

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.

Se Det I Aktion

Prøv vores PII detektion og anonymisering gratis med 200 tokens pr. cyklus.

Opret Gratis KontoHurtig Startguide

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.