Cum Funcționează anonym.legal
Detectare PII deterministă, bazată pe regex, care oferă rezultate 100% reproducibile. Aceleași date de intrare, aceeași ieșire—de fiecare dată. Fără AI, fără presupuneri, doar potrivire transparentă a modelului.
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.
De ce Regex, Nu AI?
Abordarea Noastră
- Rezultate 100% reproducibile
- Complet auditabil pentru conformitate
- Fără date de antrenament necesare
- Decizii transparente
- Performanță rapidă și predictibilă
- Fără drift al modelului în timp
Abordări AI/ML
- Rezultatele variază între execuții
- Decizii în cutie neagră
- Necesită date de antrenament
- Dificil de auditat
- Costuri de calcul mai mari
- Drift al modelului în timp
Procesul în 10 Pași
De la intrare la ieșire, iată exact ce se întâmplă cu documentul dumneavoastră
Text de Intrare
Trimiteți documentul prin intermediul interfeței web, API-ului sau Add-in-ului Office
Detectare Limbă
Sistemul identifică limba documentului pentru procesare optimă
Tokenizare
Textul este împărțit în tokenuri pentru potrivirea modelului
Potrivire a Modelului
Modelele regex scanează pentru 285+ tipuri de entități
Analiza Contextului
Textul înconjurător îmbunătățește acuratețea detectării
Scor de Încredere
Fiecare detectare primește un scor de încredere
Clasificarea Entităților
Elementele detectate sunt clasificate după tip
Revizuirea Rezultatelor
Vizualizați toate detectările cu poziții și scoruri
Aplicarea Anonimizării
Alegeți metoda dumneavoastră: Înlocuire, Redactare, Hash, Criptare sau Mască
Document de Ieșire
Descărcați documentul dumneavoastră anonimizat
Server MCP: Integrare AI cu Prioritate pe Confidențialitate
Cum datele dumneavoastră circulă prin Serverul MCP pentru a menține instrumentele AI în siguranță
Cererea Instrumentului AI
Instrumentul dumneavoastră AI (Cursor, Claude) trimite o cerere conținând PII
Serverul MCP Interceptează
Serverul analizează și detectează toate entitățile PII
Anonimizare
PII este înlocuit cu tokenuri sau redactat
Procesare AI
AI primește și procesează doar date anonimizate
Răspunsul Revine
Răspunsul AI revine prin Serverul MCP
De-tokenizare
Opțional: Valorile originale sunt restaurate pentru utilizator
Exemplu din Viața Reală
Procesează plata pentru John Doe, email john@example.com, card 4532-1111-2222-3333Ce vede AI
Procesează plata pentru PII_PERSON_001, email PII_EMAIL_001, card PII_CREDIT_CARD_001Ce primiți înapoi
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.
Vedeți-l în Acțiune
Încercați detectarea și anonimizarea PII gratuit cu 200 de tokenuri pe ciclu.
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.