anonym.legal

Nola Funtzionatzen Du anonym.legal

Deterministikoa, regex oinarritutako PII detekzioa 100% erreproduzitzeko emaitzak ematen dituena. Sarrera bera, irteera bera—beti. Ez AI, ez irudikapenik, soilik patroi gardenak.

Probatu DoanDokumentazio Teknikoa

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.

Zergatik Regex, Ez AI?

Gure Hurbilketa

  • 100% erreproduzitzeko emaitzak
  • Osorik auditatzen da betetzeko
  • Ez da prestakuntza daturik behar
  • Erabaki gardenak
  • Azkar, aurreikusteko errendimendua
  • Ez da modeloen aldaketa denboran

AI/ML Hurbilketak

  • Emaitzak aldatu egiten dira exekuzioen artean
  • Kutxa beltz erabakiak
  • Prestakuntza datuak behar ditu
  • Auditatzea zaila da
  • Kostu handiak
  • Modeloen aldaketa denboran
Ikasi gehiago gure teknologiari buruz

10 Pausoko Prozesua

Sarreratik irteerara, hemen zehazki zer gertatzen den zure dokumentuan

1

Sarrera Testua

Bidali zure dokumentua web interfazearen, APIaren edo Office Add-in bidez

2

Hizkuntza Detekzioa

Sistema dokumentuaren hizkuntza identifikatzen du prozesamendu optimoa lortzeko

3

Tokenizazioa

Testua tokenetan apurtzen da patroiak matchatzeko

4

Patroiak Matchatzea

Regex patroiak 285+ entitate mota bilatzen dituzte

5

Testuinguru Analisia

Inguru testuak detekzio zehaztasuna hobetzen du

6

Konfiantza Puntuak

Detekzio bakoitzak konfiantza puntu bat jasotzen du

7

Entitate Klasifikazioa

Detektatutako elementuak mota baten arabera kategoriza daitezke

8

Emaitzak Aztertu

Ikusi detekzio guztiak posizio eta puntuazioekin

9

Anonimizazioa Aplikatu

Hautatu zure metodoa: Ordezkatu, Ezabatu, Hash, Enkriptatu, edo Maskaratu

10

Irteera Dokumentua

Deskargatu zure anonimizatutako dokumentua

Pro eta Negozio planetan bakarrik eskuragarri

MCP Zerbitzaria: Pribatutasun-Lehen AI Integrazioa

Nola iragazten den zure datua MCP Zerbitzaritik AI tresnak seguru mantentzeko

1

AI Tresna Eskaera

Zure AI tresna (Cursor, Claude) PII duten eskaera bat bidaltzen du

2

MCP Zerbitzaria Iragazten

Zerbitzariak PII entitate guztiak aztertzen eta detektatzen ditu

3

Anonimizazioa

PII tokenekin ordezkatzen da edo ezabatzen da

Safe data only
4

AI Prozesamendua

AIk soilik anonimizatutako datuak jasotzen eta prozesatzen ditu

5

Erantzun Itzultzea

AI erantzuna MCP Zerbitzariaren bidez itzultzen da

6
Optional

De-tokenizazioa

Aukerakoa: Balio originalak erabiltzailearentzat berreskuratzen dira

Errealitateko Adibidea

Aurretik (PII-rekin)
John Doe-ren ordainketa prozesatu, email john@example.com, txartela 4532-1111-2222-3333

AIk ikusten duena

Ondoren (anonimizatua)
PII_PERSON_001-ren ordainketa prozesatu, email PII_EMAIL_001, txartela PII_CREDIT_CARD_001

Zuretzat itzultzen dena

AIk ez du inoiz zure benetako PII ikusten
Tokenizazio moduan itzuli daiteke
Web aplikazioarekin berdinak diren token kostuak
AI tresna anitzekin funtzionatzen du
Enpresako segurtasuna
Ikasi gehiago MCP Zerbitzariari buruz →

Aztertu Gehiago

Teknologia

Regex oinarritutako detekzioan sakondu eta zergatik den betetzeko hobea

Arkitektura

Sistema arkitektura eta nola osagaiak elkarrekin funtzionatzen duten

Segurtasuna

Zure datuak babesteko bost segurtasun geruza

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.

Ikusi Ekintzan

Probatu gure PII detekzioa eta anonimizazioa doan 200 token ziklo bakoitzeko.

Sortu Doako KontuaAzkar Hasierako Gida

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.