Com Funciona anonym.legal
Detecció de PII basada en regex i determinista que ofereix resultats 100% reproduïbles. Mateix input, mateix output—cada vegada. Sense IA, sense endevinalles, només coincidència de patrons transparent.
How Does PII Detection Work?
PII detection identifies personal data in text using pattern matching and machine learning. anonym.legal uses a hybrid approach:
- 1Pattern Matching: Regex patterns detect structured data (SSNs, credit cards, IBANs) with checksum validation.
- 2Named Entity Recognition: NER models identify names, locations, and organizations in 48 languages.
- 3Context 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.
Per què Regex, No IA?
El Nostre Enfocament
- Resultats 100% reproduïbles
- Totalment auditable per a compliment
- No es requereix dades d'entrenament
- Decisions transparents
- Rendiment ràpid i previsible
- Sense deriva del model amb el temps
Enfocaments IA/ML
- Els resultats varien entre execucions
- Decisions de caixa negra
- Requereix dades d'entrenament
- Difícil d'auditar
- Costos computacionals més alts
- Deriva del model amb el temps
El Procés de 10 Passos
Del input a l'output, aquí teniu exactament què passa amb el vostre document
Text d'Entrada
Envieu el vostre document a través de la interfície web, API o complement d'Office
Detecció de Llengua
El sistema identifica la llengua del document per a un processament òptim
Tokenització
El text es divideix en tokens per a la coincidència de patrons
Coincidència de Patrons
Els patrons regex escanejen més de 50 tipus d'entitats
Anàlisi de Context
El text circumdant millora l'exactitud de la detecció
Puntuació de Confiança
Cada detecció rep una puntuació de confiança
Classificació d'Entitats
Els elements detectats es classifiquen per tipus
Revisar Resultats
Vegeu totes les deteccions amb posicions i puntuacions
Aplicar Anonimització
Trieu el vostre mètode: Substituir, Redactar, Hash, Encriptar o Màscara
Document d'Output
Descarregueu el vostre document anonimitzat
Servidor MCP: Integració d'IA Centrada en la Privacitat
Com flueixen les vostres dades a través del Servidor MCP per mantenir les eines d'IA segures
Sol·licitud de l'Eina d'IA
La vostra eina d'IA (Cursor, Claude) envia una sol·licitud que conté PII
El Servidor MCP Intercepta
El servidor analitza i detecta totes les entitats PII
Anonimització
La PII es reemplaça amb tokens o es redacta
Processament d'IA
L'IA rep i processa només dades anonimitzades
Retorn de Resposta
La resposta de l'IA torna a través del Servidor MCP
De-tokenització
Opcional: Valors originals restaurats per a l'usuari
Exemple del Món Real
Processar pagament per John Doe, correu electrònic john@example.com, targeta 4532-1111-2222-3333El que veu l'IA
Processar pagament per PII_PERSON_001, correu electrònic PII_EMAIL_001, targeta PII_CREDIT_CARD_001El que obtens de tornada
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.
Veure-ho en Acció
Proveu la nostra detecció de PII i anonimització de manera gratuïta amb 200 tokens per cicle.
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
- Common questions
- Glossary
- How tokens work
- Security posture
- Where we comply
- What we detect
- Case studies
- Release notes
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
- Open the web app and try a sample file.
- Learn how credits get counted.
- See current plans and limits.
- Meet the team behind the product.
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.