anonym.legal

Cara anonym.legal Berfungsi

Pengesanan PII berasaskan regex yang deterministik yang memberikan hasil yang boleh diulang 100%. Input yang sama, output yang sama—setiap kali. Tiada AI, tiada tekaan, hanya pemadanan corak yang telus.

Cuba Secara PercumaDokumentasi Teknikal

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.

Mengapa Regex, Bukan AI?

Pendekatan Kami

  • Hasil yang boleh diulang 100%
  • Sepenuhnya boleh diaudit untuk pematuhan
  • Tiada data latihan diperlukan
  • Keputusan yang telus
  • Prestasi yang cepat dan boleh diramal
  • Tiada perubahan model dari semasa ke semasa

Pendekatan AI/ML

  • Hasil berbeza antara larian
  • Keputusan kotak hitam
  • Memerlukan data latihan
  • Sukar untuk diaudit
  • Kos pengiraan yang lebih tinggi
  • Perubahan model dari semasa ke semasa
Ketahui lebih lanjut tentang teknologi kami

Proses 10 Langkah

Dari input ke output, inilah yang berlaku kepada dokumen anda

1

Input Teks

Hantar dokumen anda melalui antara muka web, API, atau Tambahan Office

2

Pengesanan Bahasa

Sistem mengenal pasti bahasa dokumen untuk pemprosesan yang optimum

3

Tokenisasi

Teks dipecahkan kepada token untuk pemadanan corak

4

Pemadanan Corak

Corak regex mengimbas untuk 285+ jenis entiti

5

Analisis Konteks

Teks sekeliling meningkatkan ketepatan pengesanan

6

Skor Keyakinan

Setiap pengesanan menerima skor keyakinan

7

Klasifikasi Entiti

Item yang dikesan dikategorikan mengikut jenis

8

Semak Hasil

Lihat semua pengesanan dengan kedudukan dan skor

9

Terapkan Pengaburan

Pilih kaedah anda: Gantikan, Redak, Hash, Enkripsi, atau Mask

10

Dokumen Output

Muat turun dokumen yang telah dianonimkan

Tersedia hanya pada pelan Pro dan Perniagaan

Pelayan MCP: Integrasi AI Berfokus Privasi

Bagaimana data anda mengalir melalui Pelayan MCP untuk menjaga keselamatan alat AI

1

Permintaan Alat AI

Alat AI anda (Cursor, Claude) menghantar permintaan yang mengandungi PII

2

Pelayan MCP Menyekat

Pelayan menganalisis dan mengesan semua entiti PII

3

Pengaburan

PII digantikan dengan token atau disunting

Safe data only
4

Pemprosesan AI

AI menerima dan memproses hanya data yang telah dianonimkan

5

Kembalikan Respons

Respons AI kembali melalui Pelayan MCP

6
Optional

De-tokenisasi

Pilihan: Nilai asal dipulihkan untuk pengguna

Contoh Dunia Nyata

Sebelum (dengan PII)
Proses pembayaran untuk John Doe, email john@example.com, kad 4532-1111-2222-3333

Apa yang dilihat AI

Selepas (dianonimkan)
Proses pembayaran untuk PII_PERSON_001, email PII_EMAIL_001, kad PII_CREDIT_CARD_001

Apa yang anda dapat kembali

AI tidak pernah melihat PII sebenar anda
Boleh dipulihkan dengan mod tokenisasi
Kos token yang sama seperti aplikasi web
Berfungsi dengan pelbagai alat AI
Keselamatan tahap perusahaan
Ketahui lebih lanjut tentang Pelayan MCP →

Jelajahi Lebih Lanjut

Teknologi

Mendalami pengesanan berasaskan regex dan mengapa ia lebih baik untuk pematuhan

Arsitektur

Arsitektur sistem dan bagaimana komponen berfungsi bersama

Keselamatan

Lima lapisan keselamatan melindungi data anda di setiap langkah

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.

Lihat Ia Dalam Tindakan

Cuba pengesanan dan pengaburan PII kami secara percuma dengan 200 token setiap kitaran.

Buat Akaun PercumaPanduan Permulaan Pantas

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.