anonym.legal

Kako funkcioniše anonym.legal

Deterministička, regex-zasnovana PII detekcija koja pruža 100% reproduktivne rezultate. Isti ulaz, isti izlaz—svaki put. Bez AI, bez nagađanja, samo transparentno prepoznavanje obrazaca.

Isprobajte besplatnoTehnička dokumentacija

How Does PII Detection Work?

PII detection identifies personal data in text using pattern matching and machine learning. anonym.legal uses a hybrid approach:

  1. 1
    Pattern Matching: Regex patterns detect structured data (SSNs, credit cards, IBANs) with checksum validation.
  2. 2
    Named Entity Recognition: NER models identify names, locations, and organizations in 48 languages.
  3. 3
    Context 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.

Zašto Regex, a ne AI?

Naš pristup

  • 100% reproduktivni rezultati
  • Potpuno auditable za usklađenost
  • Nema potrebnih podataka za obuku
  • Transparentno donošenje odluka
  • Brza, predvidiva performansa
  • Nema driftanja modela tokom vremena

AI/ML pristupi

  • Rezultati variraju između izvršavanja
  • Crna kutija donošenja odluka
  • Zahteva podatke za obuku
  • Teško je auditable
  • Viši troškovi obrade
  • Driftanje modela tokom vremena
Saznajte više o našoj tehnologiji

Proces u 10 koraka

Od ulaza do izlaza, evo šta se tačno dešava sa vašim dokumentom

1

Ulazni tekst

Pošaljite svoj dokument putem web interfejsa, API-a ili Office dodatka

2

Detekcija jezika

Sistem identifikuje jezik dokumenta za optimalnu obradu

3

Tokenizacija

Tekst se deli na tokene za prepoznavanje obrazaca

4

Prepoznavanje obrazaca

Regex obrasci skeniraju za 285+ tipova entiteta

5

Analiza konteksta

Okolni tekst poboljšava tačnost detekcije

6

Ocena poverenja

Svaka detekcija dobija ocenu poverenja

7

Klasifikacija entiteta

Detektovani predmeti se kategorizuju po tipu

8

Pregled rezultata

Pogledajte sve detekcije sa pozicijama i ocenama

9

Primena anonimizacije

Izaberite svoju metodu: Zamena, Redigovanje, Hash, Enkripcija ili Maskiranje

10

Izlazni dokument

Preuzmite svoj anonimizovani dokument

Dostupno samo na Pro i Business planovima

MCP Server: Integracija AI sa fokusom na privatnost

Kako vaši podaci prolaze kroz MCP Server da bi se AI alati čuvali

1

Zahtev AI alata

Vaš AI alat (Cursor, Claude) šalje zahtev koji sadrži PII

2

MCP Server presreće

Server analizira i detektuje sve PII entitete

3

Anonimizacija

PII se zamenjuje tokenima ili rediguje

Safe data only
4

AI obrada

AI prima i obrađuje samo anonimizovane podatke

5

Vraćanje odgovora

AI odgovor se vraća kroz MCP Server

6
Optional

De-tokenizacija

Opcionalno: Originalne vrednosti vraćene korisniku

Primer iz stvarnog sveta

Pre (sa PII)
Obradite uplatu za John Doe, email john@example.com, kartica 4532-1111-2222-3333

Šta AI vidi

Posle (anonimizovano)
Obradite uplatu za PII_PERSON_001, email PII_EMAIL_001, kartica PII_CREDIT_CARD_001

Šta dobijate nazad

AI nikada ne vidi vaš pravi PII
Reverzibilno sa načinom tokenizacije
Isti troškovi tokena kao web aplikacija
Radi sa više AI alata
Bezbednost na nivou preduzeća
Saznajte više o MCP Server-u →

Istražite dalje

Tehnologija

Duboko zaranjanje u regex-zasnovanu detekciju i zašto je bolja za usklađenost

Arhitektura

Sistematska arhitektura i kako komponente rade zajedno

Bezbednost

Pet slojeva bezbednosti štiti vaše podatke na svakom koraku

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.

Pogledajte u akciji

Isprobajte našu PII detekciju i anonimizaciju besplatno sa 200 tokena po ciklusu.

Kreirajte besplatan nalogBrzi vodič

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

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

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.