anonym.legal

Jinsi anonym.legal Inavyofanya Kazi

Ugunduzi wa PII wa kisayansi, unaotegemea regex unaotoa matokeo ya 100% yanayoweza kurudiwa. Ingizo sawa, matokeo sawa—kila wakati. Hakuna AI, hakuna kukisia, tu ulinganifu wa wazi wa mifumo.

Jaribu BureDokumentasih ya Kitaalamu

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.

Kwa Nini Regex, Si AI?

Mbinu Yetu

  • Matokeo ya 100% yanayoweza kurudiwa
  • Kamili inayoangaliwa kwa ajili ya kufuata sheria
  • Haitaji data ya mafunzo
  • Uamuzi wa wazi
  • Utendaji wa haraka, unaoweza kutabiriwa
  • Hakuna mabadiliko ya mfano kwa muda

Mbinu za AI/ML

  • Matokeo yanatofautiana kati ya kukimbia
  • Uamuzi wa sanduku jeusi
  • Inahitaji data ya mafunzo
  • Ngumu kuangalia
  • Gharama kubwa za kompyuta
  • Mabadiliko ya mfano kwa muda
Jifunze zaidi kuhusu teknolojia yetu

Mchakato wa Hatua 10

Kutoka ingizo hadi matokeo, hapa kuna kile kinachotokea kwa hati yako

1

Ingizo la Teksti

Tuma hati yako kupitia kiolesura cha wavuti, API, au Office Add-in

2

Ugunduzi wa Lugha

Mfumo unatambua lugha ya hati kwa ajili ya usindikaji bora

3

Tokenization

Teksti inavunjwa kuwa tokeni kwa ajili ya ulinganifu wa mifumo

4

Ulinganifu wa Mifumo

Mifumo ya regex inatafuta aina 285+ za vitu

5

Analizi ya Muktadha

Teksti inayozunguka inaboresha usahihi wa ugunduzi

6

Alama ya Kujiamini

Kila ugunduzi unapata alama ya kujiamini

7

Uainishaji wa Vitu

Vitu vilivyogunduliwa vinapangwa kwa aina

8

Kagua Matokeo

Tazama ugunduzi wote pamoja na nafasi na alama

9

Tumia Uondoaji

Chagua njia yako: Badilisha, Ficha, Hash, Ficha, au Ficha

10

Hati ya Matokeo

Pakua hati yako iliyofichwa

Inapatikana kwenye mipango ya Pro na Biashara pekee

MCP Server: Uunganisho wa AI wa Kwanza wa Faragha

Jinsi data yako inavyopita kupitia MCP Server ili kuweka zana za AI salama

1

Omba Zana ya AI

Zana yako ya AI (Cursor, Claude) inatuma ombi lenye PII

2

MCP Server Inakamatwa

Server inachambua na kugundua vitu vyote vya PII

3

Uondoaji

PII inabadilishwa na tokeni au kufichwa

Safe data only
4

Usindikaji wa AI

AI inapata na kusindika tu data iliyofichwa

5

Jibu Linarudi

Jibu la AI linarudi kupitia MCP Server

6
Optional

Kurejesha Tokeni

Hiari: Thamani za asili zinaweza kurejeshwa kwa mtumiaji

Mfano wa Uhalisia

Kabla (ikiwa na PII)
Fanya malipo kwa John Doe, barua pepe john@example.com, kadi 4532-1111-2222-3333

Kile AI inaona

Baada (iliyofichwa)
Fanya malipo kwa PII_PERSON_001, barua pepe PII_EMAIL_001, kadi PII_CREDIT_CARD_001

Kile unachopata

AI haioni PII yako halisi
Inaweza kurekebishwa kwa njia ya tokenization
Gharama sawa za tokeni kama programu ya wavuti
Inafanya kazi na zana nyingi za AI
Usalama wa kiwango cha biashara
Jifunze zaidi kuhusu MCP Server →

Chunguza Zaidi

Teknolojia

Chunguza kwa undani ugunduzi wa msingi wa regex na kwa nini ni bora kwa kufuata sheria

Mifumo

Mifumo ya mfumo na jinsi vipengele vinavyofanya kazi pamoja

Usalama

Tabaka tano za usalama zinazolinda data yako katika kila hatua

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.

Ione Katika Vitendo

Jaribu ugunduzi wetu wa PII na uondoaji bure kwa tokeni 200 kwa mzunguko.

Unda Akaunti BureMwongozo wa Kuanzia Haraka

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.