Kaip veikia anonym.legal
Deterministinė, regex pagrindu veikianti PII detekcija, kuri suteikia 100% atkartojamus rezultatus. Tas pats įvestis, tas pats išvestis—kiekvieną kartą. Jokios AI, jokio spėjimo, tik skaidrus modelių atitikimas.
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.
Kodėl Regex, o ne AI?
Mūsų požiūris
- 100% atkartojami rezultatai
- Visiškai audituojami atitikties tikslais
- Nereikia mokymo duomenų
- Skaidrus sprendimų priėmimas
- Greitas, prognozuojamas našumas
- Nėra modelio nuokrypio laikui bėgant
AI/ML požiūriai
- Rezultatai skiriasi tarp vykdymų
- Juodosios dėžės sprendimų priėmimas
- Reikalingi mokymo duomenys
- Sunku audituoti
- Aukštesnės skaičiavimo išlaidos
- Modelio nuokrypis laikui bėgant
10 žingsnių procesas
Nuo įvesties iki išvesties, štai kas vyksta su jūsų dokumentu
Įvesties tekstas
Pateikite savo dokumentą per interneto sąsają, API arba Office Add-in
Kalbos aptikimas
Sistema nustato dokumento kalbą optimaliam apdorojimui
Tokenizacija
Tekstas padalijamas į tokenus modelių atitikimui
Modelių atitikimas
Regex modeliai tikrina 285+ subjektų tipų
Konteksto analizė
Aplinkinis tekstas pagerina detekcijos tikslumą
Pasitikėjimo balai
Kiekviena detekcija gauna pasitikėjimo balą
Subjektų klasifikacija
Aptikti elementai klasifikuojami pagal tipą
Peržiūrėti rezultatus
Peržiūrėkite visus aptikimus su pozicijomis ir balais
Taikyti anonimizavimą
Pasirinkite savo metodą: Pakeisti, Redaguoti, Hash, Šifruoti arba Maskuoti
Išvesties dokumentas
Atsisiųskite savo anonimizuotą dokumentą
MCP serveris: Privatumo pirmas AI integracija
Kaip jūsų duomenys teka per MCP serverį, kad būtų saugūs AI įrankiai
AI įrankio užklausa
Jūsų AI įrankis (Cursor, Claude) siunčia užklausą, kurioje yra PII
MCP serveris perima
Serveris analizuoja ir aptinka visus PII subjektus
Anonimizavimas
PII yra pakeičiama tokenais arba redaguojama
AI apdorojimas
AI gauna ir apdoroja tik anonimizuotus duomenis
Atsakymo grąžinimas
AI atsakymas grįžta per MCP serverį
De-tokenizacija
Pasirinktinai: originalios vertės atkuriamos vartotojui
Realių pavyzdžių
Apdorokite mokėjimą už John Doe, el. paštas john@example.com, kortelė 4532-1111-2222-3333Ką AI mato
Apdorokite mokėjimą už PII_PERSON_001, el. paštas PII_EMAIL_001, kortelė PII_CREDIT_CARD_001Ką jūs gaunate atgal
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.
Pamatykite tai veikiant
Išbandykite mūsų PII detekciją ir anonimizavimą nemokamai su 200 tokenų per ciklą.
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.