By George Curta · Last updated 2026-04-07
PII-Erkennung: 285+ Entitätstypen
Unsere regex-basierte Erkennungsengine identifiziert über 285 Arten von persönlichen Informationen mit musterbasierter Präzision. Gleiche Eingabe, gleiche Ausgabe - jedes Mal.
Wie die Erkennung funktioniert
Mustererkennung
Verwendet sorgfältig ausgearbeitete regex-Muster für jeden Entitätstyp und gewährleistet konsistente und vorhersehbare Ergebnisse in allen Dokumenten.
Vertrauensbewertung
Jede Erkennung enthält einen Vertrauensscore (0-1), basierend auf der Musterstärke und dem Kontext, der Ihnen hilft, Ergebnisse zu filtern.
Kontextbewusstsein
Der umgebende Textkontext verbessert die Erkennungsgenauigkeit, reduziert Fehlalarme und erfasst Randfälle.
Supported Entity Types
Comprehensive coverage of personal information types across categories
Personal Identifiers
- Person Names
- Email Addresses
- Phone Numbers
- Date of Birth
- Age
- Gender
- Nationality
Financial Information
- Credit Card Numbers
- IBAN
- BIC/SWIFT
- Bank Account Numbers
- Tax IDs
- VAT Numbers
Government IDs
- Social Security Numbers (SSN)
- National ID Numbers
- Passport Numbers
- Driver's License
- Health Insurance IDs
Location Data
- Street Addresses
- Cities
- ZIP/Postal Codes
- Countries
- GPS Coordinates
Digital Identifiers
- IP Addresses (v4/v6)
- MAC Addresses
- URLs
- Domain Names
- User IDs
Organization Data
- Company Names
- Organization IDs
- Registration Numbers
- Department Names
Temporal Data
- Dates
- Times
- Date Ranges
- Timestamps
International Formats
- German ID (Personalausweis)
- UK National Insurance
- Spanish DNI/NIE
- Italian Codice Fiscale
- And 20+ more country-specific formats
Unterstützung für benutzerdefinierte Entitäten
Müssen Sie benutzerdefinierte Muster erkennen? Erstellen Sie Ihre eigenen Entitätstypen mit regex-Mustern oder verwenden Sie unseren KI-unterstützten Muster-Generator.
Manuelle Mustererstellung
Definieren Sie regex-Muster für proprietäre Identifikatoren wie interne Mitarbeiter-IDs, Projektcodes oder benutzerdefinierte Referenznummern.
KI-Muster-Generator
Beschreiben Sie, was Sie erkennen möchten, in einfacher Sprache, und unsere KI generiert optimierte regex-Muster für Sie.
Beginnen Sie noch heute mit der PII-Erkennung
Testen Sie unsere Erkennungsengine kostenlos mit 200 Tokens pro Zyklus. Keine Kreditkarte erforderlich.
Kostenloses Konto erstellenAbout 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.