Comment anonym.legal fonctionne
Détection PII déterministe basée sur regex qui fournit des résultats 100 % reproductibles. Même entrée, même sortie—à chaque fois. Pas d'IA, pas de devinette, juste un appariement de motifs 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.
Pourquoi Regex, pas IA ?
Notre approche
- Résultats 100 % reproductibles
- Entièrement auditable pour la conformité
- Pas de données d'entraînement requises
- Prise de décision transparente
- Performance rapide et prévisible
- Pas de dérive de modèle au fil du temps
Approches IA/ML
- Les résultats varient entre les exécutions
- Prise de décision en boîte noire
- Nécessite des données d'entraînement
- Difficile à auditer
- Coûts de calcul plus élevés
- Dérive de modèle au fil du temps
Le processus en 10 étapes
De l'entrée à la sortie, voici exactement ce qui se passe avec votre document
Texte d'entrée
Soumettez votre document via l'interface web, l'API ou le module complémentaire Office
Détection de la langue
Le système identifie la langue du document pour un traitement optimal
Tokenisation
Le texte est divisé en jetons pour l'appariement de motifs
Appariement de motifs
Les motifs regex analysent plus de 50 types d'entités
Analyse contextuelle
Le texte environnant améliore la précision de la détection
Évaluation de la confiance
Chaque détection reçoit un score de confiance
Classification des entités
Les éléments détectés sont classés par type
Examiner les résultats
Voir toutes les détections avec positions et scores
Appliquer l'anonymisation
Choisissez votre méthode : Remplacer, Rédiger, Hacher, Chiffrer ou Masquer
Document de sortie
Téléchargez votre document anonymisé
Serveur MCP : Intégration AI axée sur la confidentialité
Comment vos données circulent à travers le serveur MCP pour garder les outils AI en sécurité
Demande de l'outil AI
Votre outil AI (Cursor, Claude) envoie une demande contenant des PII
Le serveur MCP intercepte
Le serveur analyse et détecte toutes les entités PII
Anonymisation
Les PII sont remplacés par des jetons ou rédigés
Traitement AI
L'IA reçoit et traite uniquement des données anonymisées
Retour de réponse
La réponse de l'IA revient via le serveur MCP
Détokenisation
Optionnel : Valeurs originales restaurées pour l'utilisateur
Exemple du monde réel
Traiter le paiement pour John Doe, e-mail john@example.com, carte 4532-1111-2222-3333Ce que l'IA voit
Traiter le paiement pour PII_PERSON_001, e-mail PII_EMAIL_001, carte PII_CREDIT_CARD_001Ce que vous obtenez en retour
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.
Voyez-le en action
Essayez notre détection et anonymisation PII gratuitement avec 200 jetons par cycle.
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.