Wie anonym.legal funktioniert
Deterministische, regex-basierte PII-Erkennung, die 100% reproduzierbare Ergebnisse liefert. Gleiche Eingabe, gleiche Ausgabe – jedes Mal. Keine KI, kein Raten, nur transparente Mustererkennung.
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.
Warum Regex, nicht KI?
Unser Ansatz
- 100% reproduzierbare Ergebnisse
- Vollständig prüfbar für die Einhaltung
- Keine Trainingsdaten erforderlich
- Transparente Entscheidungsfindung
- Schnelle, vorhersehbare Leistung
- Kein Modellabdrift im Laufe der Zeit
KI/ML-Ansätze
- Ergebnisse variieren zwischen den Durchläufen
- Black-Box-Entscheidungsfindung
- Benötigt Trainingsdaten
- Schwer zu prüfen
- Höhere Rechenkosten
- Modellabdrift im Laufe der Zeit
Der 10-Schritte-Prozess
Von der Eingabe bis zur Ausgabe, hier ist genau, was mit Ihrem Dokument passiert
Eingabetext
Reichen Sie Ihr Dokument über die Weboberfläche, API oder Office-Add-In ein
Spracherkennung
Das System identifiziert die Dokumentensprache für eine optimale Verarbeitung
Tokenisierung
Der Text wird in Tokens für die Mustererkennung zerlegt
Mustererkennung
Regex-Muster scannen nach 285+ Entitätstypen
Kontextanalyse
Umgebender Text verbessert die Erkennungsgenauigkeit
Vertrauensbewertung
Jede Erkennung erhält eine Vertrauensbewertung
Entitätsklassifizierung
Erkannte Elemente werden nach Typ kategorisiert
Ergebnisse überprüfen
Sehen Sie alle Erkennungen mit Positionen und Bewertungen
Anonymisierung anwenden
Wählen Sie Ihre Methode: Ersetzen, Schwärzen, Hashen, Verschlüsseln oder Maskieren
Ausgabedokument
Laden Sie Ihr anonymisiertes Dokument herunter
MCP-Server: Datenschutzorientierte KI-Integration
Wie Ihre Daten durch den MCP-Server fließen, um KI-Tools sicher zu halten
KI-Tool-Anfrage
Ihr KI-Tool (Cursor, Claude) sendet eine Anfrage mit PII
MCP-Server fängt ab
Server analysiert und erkennt alle PII-Entitäten
Anonymisierung
PII wird durch Tokens ersetzt oder geschwärzt
KI-Verarbeitung
KI erhält und verarbeitet nur anonymisierte Daten
Antwort zurückgeben
KI-Antwort kommt über den MCP-Server zurück
De-Tokenisierung
Optional: Ursprüngliche Werte für den Benutzer wiederhergestellt
Beispiel aus der Praxis
Zahlung für John Doe verarbeiten, E-Mail john@example.com, Karte 4532-1111-2222-3333Was die KI sieht
Zahlung für PII_PERSON_001 verarbeiten, E-Mail PII_EMAIL_001, Karte PII_CREDIT_CARD_001Was Sie zurückbekommen
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.
Sehen Sie es in Aktion
Testen Sie unsere PII-Erkennung und Anonymisierung kostenlos mit 200 Tokens pro Zyklus.
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.