anonym.legal

Cara Kerja anonym.legal

Deteksi PII berbasis regex yang deterministik yang memberikan hasil 100% dapat direproduksi. Input yang sama, output yang sama—setiap saat. Tanpa AI, tanpa tebak-tebakan, hanya pencocokan pola yang transparan.

Coba GratisDokumentasi Teknis

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 100% dapat direproduksi
  • Sepenuhnya dapat diaudit untuk kepatuhan
  • Tidak memerlukan data pelatihan
  • Pengambilan keputusan yang transparan
  • Kinerja cepat dan dapat diprediksi
  • Tidak ada pergeseran model seiring waktu

Pendekatan AI/ML

  • Hasil bervariasi antara percobaan
  • Pengambilan keputusan kotak hitam
  • Memerlukan data pelatihan
  • Sulit diaudit
  • Biaya komputasi lebih tinggi
  • Pergeseran model seiring waktu
Pelajari lebih lanjut tentang teknologi kami

Proses 10 Langkah

Dari input ke output, berikut adalah apa yang terjadi pada dokumen Anda

1

Input Teks

Kirim dokumen Anda melalui antarmuka web, API, atau Office Add-in

2

Deteksi Bahasa

Sistem mengidentifikasi bahasa dokumen untuk pemrosesan yang optimal

3

Tokenisasi

Teks dipecah menjadi token untuk pencocokan pola

4

Pencocokan Pola

Pola regex memindai lebih dari 50 jenis entitas

5

Analisis Konteks

Teks di sekitar meningkatkan akurasi deteksi

6

Penilaian Kepercayaan

Setiap deteksi menerima skor kepercayaan

7

Klasifikasi Entitas

Item yang terdeteksi dikategorikan berdasarkan jenis

8

Tinjau Hasil

Lihat semua deteksi dengan posisi dan skor

9

Terapkan Anonimisasi

Pilih metode Anda: Ganti, Redak, Hash, Enkripsi, atau Masker

10

Dokumen Output

Unduh dokumen Anda yang telah dianonimkan

Tersedia hanya pada paket Pro dan Bisnis

MCP Server: Integrasi AI Berfokus pada Privasi

Bagaimana data Anda mengalir melalui MCP Server untuk menjaga alat AI tetap aman

1

Permintaan Alat AI

Alat AI Anda (Cursor, Claude) mengirim permintaan yang berisi PII

2

MCP Server Mencegat

Server menganalisis dan mendeteksi semua entitas PII

3

Anonimisasi

PII diganti dengan token atau dihapus

Safe data only
4

Pemrosesan AI

AI menerima dan memproses hanya data yang dianonimkan

5

Pengembalian Respon

Respon AI kembali melalui MCP Server

6
Optional

De-tokenisasi

Opsional: Nilai asli dikembalikan untuk pengguna

Contoh Dunia Nyata

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

Apa yang dilihat AI

Setelah (dianonimkan)
Proses pembayaran untuk PII_PERSON_001, email PII_EMAIL_001, kartu PII_CREDIT_CARD_001

Apa yang Anda dapatkan kembali

AI tidak pernah melihat PII asli Anda
Dapat dibalik dengan mode tokenisasi
Biaya token yang sama seperti aplikasi web
Bekerja dengan beberapa alat AI
Keamanan tingkat perusahaan
Pelajari lebih lanjut tentang MCP Server →

Jelajahi Lebih Lanjut

Teknologi

Mendalami deteksi berbasis regex dan mengapa ini lebih baik untuk kepatuhan

Arsitektur

Arsitektur sistem dan bagaimana komponen bekerja bersama

Keamanan

Lima lapisan keamanan 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 Dalam Aksi

Coba deteksi PII dan anonimisasi kami secara gratis dengan 200 token per siklus.

Buat Akun GratisPanduan Memulai Cepat

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.