anonym.legal

Hur anonym.legal Fungerar

Deterministisk, regex-baserad PII-upptäckte som ger 100% reproducerbara resultat. Samma indata, samma utdata—varje gång. Ingen AI, ingen gissning, bara transparent mönsterigenkänning.

Prova 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.

Varför Regex, Inte AI?

Vår Strategi

  • 100% reproducerbara resultat
  • Helt granskbara för efterlevnad
  • Ingen träningsdata krävs
  • Transparent beslutsfattande
  • Snabb, förutsägbar prestanda
  • Ingen modellavvikelse över tid

AI/ML Strategier

  • Resultat varierar mellan körningar
  • Svart låda beslutsfattande
  • Kräver träningsdata
  • Svårt att granska
  • Högre beräkningskostnader
  • Modellavvikelse över tid
Lär dig mer om vår teknik

10-Stegs Processen

Från indata till utdata, här är exakt vad som händer med ditt dokument

1

Indata Text

Skicka ditt dokument via webbgränssnitt, API eller Office-tillägg

2

Språkupptäckte

Systemet identifierar dokumentets språk för optimal bearbetning

3

Tokenisering

Texten bryts ner i tokens för mönsterigenkänning

4

Mönsterigenkänning

Regex-mönster skannar efter 285+ entitetstyper

5

Kontextanalys

Omgivande text förbättrar upptäcktsnoggrannheten

6

Konfidenspoäng

Varje upptäckte får en konfidenspoäng

7

Entitetsklassificering

Upptäckta objekt kategoriseras efter typ

8

Granska Resultat

Se alla upptäckter med positioner och poäng

9

Tillämpa Anonymisering

Välj din metod: Ersätt, Maskera, Hasha, Kryptera eller Maskera

10

Utdata Dokument

Ladda ner ditt anonymiserade dokument

Tillgänglig endast på Pro och Business-planer

MCP Server: Integrering av Integritetsfokuserad AI

Hur dina data flödar genom MCP Server för att hålla AI-verktyg säkra

1

AI Verktygsförfrågan

Ditt AI-verktyg (Cursor, Claude) skickar en förfrågan som innehåller PII

2

MCP Server Avlyssnar

Servern analyserar och upptäcker alla PII-entiteter

3

Anonymisering

PII ersätts med tokens eller maskeras

Safe data only
4

AI Bearbetning

AI tar emot och bearbetar endast anonymiserade data

5

Svar Återvänd

AI-svaret kommer tillbaka genom MCP Server

6
Optional

De-tokenisering

Valfritt: Ursprungliga värden återställs för användaren

Verkligt Exempel

Före (med PII)
Behandla betalning för John Doe, e-post john@example.com, kort 4532-1111-2222-3333

Vad AI ser

Efter (anonymiserad)
Behandla betalning för PII_PERSON_001, e-post PII_EMAIL_001, kort PII_CREDIT_CARD_001

Vad du får tillbaka

AI ser aldrig din verkliga PII
Reversibel med tokeniseringsläge
Samma tokenkostnader som webbapplikationen
Fungerar med flera AI-verktyg
Företagsklassad säkerhet
Lär dig mer om MCP Server →

Utforska Vidare

Teknik

Dyk djupt in i regex-baserad upptäckte och varför det är bättre för efterlevnad

Arkitektur

Systemarkitektur och hur komponenter arbetar tillsammans

Säkerhet

Fem säkerhetslager som skyddar dina data vid varje steg

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

Prova vår PII-upptäckte och anonymisering gratis med 200 tokens per cykel.

Skapa Gratis KontoSnabbstartsguide

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.