anonym.legal

Hoe anonym.legal Werk

Deterministiese, regex-gebaseerde PII-detectie wat 100% herhaalbare resultate lewer. Dieselfde invoer, dieselfde uitvoer—elke keer. Geen KI, geen raaiwerk nie, net deursigtige patroonvergelyking.

Probeer Dit GratisTegniese Dokumentasie

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, Nie KI nie?

Ons Benadering

  • 100% herhaalbare resultate
  • Volledig auditeerbaar vir nakoming
  • Geen opleidingsdata benodig nie
  • Deursigtige besluitneming
  • Vinige, voorspelbare prestasie
  • Geen modelafwyking oor tyd nie

KI/ML Benaderings

  • Resultate verskil tussen uitvoerings
  • Swart boks besluitneming
  • Benodig opleidingsdata
  • Moeilik om te auditeer
  • Hoër rekenaar koste
  • Modelafwyking oor tyd
Leer meer oor ons tegnologie

Die 10-Stap Proses

Van invoer tot uitvoer, hier is presies wat met jou dokument gebeur

1

Invoer Tekst

Dien jou dokument in via webkoppelvlak, API, of Office Add-in

2

Taal Detectie

Stelsel identifiseer die dokumenttaal vir optimale verwerking

3

Tokenisering

Tekst word in tokens gebroke vir patroonvergelyking

4

Patroonvergelyking

Regex patrone skandeer vir 285+ entiteit tipes

5

Konteksanalise

Omringende teks verbeter detectie akkuraatheid

6

Vertroue Puntetoekenning

Elke detectie ontvang 'n vertroue punt

7

Entiteit Klassifikasie

Gediagnoseerde items word volgens tipe gekategoriseer

8

Hersien Resultate

Sien al die detecties met posisies en punte

9

Pas Anonimisering Toe

Kies jou metode: Vervang, Swartmaak, Hash, Enkripteer, of Masker

10

Uitvoer Dokument

Laai jou geanonimiseerde dokument af

Slegs beskikbaar op Pro en Besigheids planne

MCP Bediening: Privaatheid-Eerste KI Integrasie

Hoe jou data deur die MCP Bediening vloei om KI gereedskap veilig te hou

1

KI Gereedskap Versoek

Jou KI gereedskap (Cursor, Claude) stuur 'n versoek wat PII bevat

2

MCP Bediening Onderbreek

Bediening analiseer en detecteer alle PII entiteite

3

Anonimisering

PII word vervang met tokens of swartgemaak

Safe data only
4

KI Verwerking

KI ontvang en verwerk slegs geanonimiseerde data

5

Antwoord Terugkeer

KI antwoord kom terug deur MCP Bediening

6
Optional

De-tokenisering

Opsioneel: Oorspronklike waardes herstel vir gebruiker

Werklike Voorbeeld

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

Wat KI sien

Na (geanonimiseer)
Verwerk betaling vir PII_PERSON_001, e-pos PII_EMAIL_001, kaart PII_CREDIT_CARD_001

Wat jy terugkry

KI sien nooit jou werklike PII nie
Herstelbaar met tokenisering modus
Dieselfde token koste as webtoepassing
Werk met verskeie KI gereedskap
Ondernemingsgraad sekuriteit
Leer meer oor MCP Bediening →

Verken Verder

Tegnologie

Diep duik in regex-gebaseerde detectie en waarom dit beter is vir nakoming

Argitektuur

Stelselargitektuur en hoe komponente saamwerk

Sekuriteit

Vyf sekuriteitslae wat jou data by elke stap beskerm

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.

Sien Dit in Aksie

Probeer ons PII-detectie en anonymisering gratis met 200 tokens per siklus.

Skep Gratis RekeningVinige Begin Gids

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.