anonym.legal

Hvernig anonym.legal virkar

Ákveðin, regex-grundvölluð PII greining sem skilar 100% endurteknum niðurstöðum. Sama inntak, sama úttak—í hvert sinn. Engin AI, engin ágiskun, bara gagnsæ mynsturgreining.

Prófaðu ókeypisTæknileg skjöl

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.

Af hverju Regex, ekki AI?

Aðferð okkar

  • 100% endurtekna niðurstöður
  • Heildar skoðanleg fyrir samræmi
  • Engin þjálfunargögn nauðsynleg
  • Gagnsæ ákvörðunartaka
  • Hratt, fyrirsjáanlegt frammistöð
  • Engin módelbreyting með tímanum

AI/ML aðferðir

  • Niðurstöður breytast milli keyrslna
  • Svartur kassi ákvörðunartaka
  • Krafist þjálfunargagna
  • Erfiðara að skoða
  • Hærri útreikningakostnaður
  • Módelbreyting með tímanum
Lærðu meira um tækni okkar

10 skref ferlið

Frá inntaki til úttaks, hér er nákvæmlega hvað gerist við skjalið þitt

1

Inntak texta

Sendu skjalið þitt í gegnum vefviðmót, API, eða Office viðbót

2

Tungumálagreining

Kerfið greinir tungumál skjalsins fyrir bestu úrvinnslu

3

Tokenization

Textinn er brotinn niður í tokens fyrir mynsturgreiningu

4

Mynsturgreining

Regex mynstur skanna fyrir 285+ tegundir eininga

5

Samhengisgreining

Umhverfishugmyndin eykur nákvæmni greiningar

6

Trauststig

Hver greining fær trauststig

7

Einingaflokkun

Greind atriði eru flokkast eftir tegund

8

Skoða niðurstöður

Sjáðu allar greiningar með stöðum og stigum

9

Beita nafnleynd

Veldu aðferðina þína: Skipta, Ritskoða, Hash, Dulkóða, eða Mask

10

Úttak skjal

Sæktu nafnleyndu skjalið þitt

Fáanlegt aðeins á Pro og Business áætlunum

MCP Server: Persónuverndarfyrst AI samþætting

Hvernig gögnin þín flæða í gegnum MCP Server til að halda AI verkfærum öruggum

1

AI verkfæra fyrirspurn

AI verkfærið þitt (Cursor, Claude) sendir fyrirspurn sem inniheldur PII

2

MCP Server grípur

Serverinn greinir og greinir allar PII einingar

3

Nafnleynd

PII er skipt út fyrir tokens eða ritskoðað

Safe data only
4

AI úrvinnsla

AI fær og úrvinnur aðeins nafnleynd gögn

5

Svar skilað

AI svar kemur aftur í gegnum MCP Server

6
Optional

De-tokenization

Valfrjálst: Upprunaleg gildi endurheimt fyrir notanda

Raunverulegt dæmi

Fyrir (með PII)
Vinnur greiðslu fyrir John Doe, tölvupóstur john@example.com, kort 4532-1111-2222-3333

Hvað AI sér

Eftir (nafnleynd)
Vinnur greiðslu fyrir PII_PERSON_001, tölvupóstur PII_EMAIL_001, kort PII_CREDIT_CARD_001

Hvað þú færð aftur

AI sér aldrei raunveruleg PII þín
Afturkræft með tokenization mód
Sömu token kostnað eins og vefumsókn
Virkar með mörgum AI verkfærum
Öryggi á fyrirtækjaskala
Lærðu meira um MCP Server →

Kannaðu frekar

Tækni

Djúpur kafli í regex-grundvallaða greiningu og hvers vegna það er betra fyrir samræmi

Skipulag

Kerfisskipulag og hvernig einingarnar vinna saman

Öryggi

Fimm öryggislög vernda gögnin þín á hverju skrefi

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.

Sjáðu það í aðgerð

Prófaðu PII greiningu okkar og nafnleynd ókeypis með 200 tokens á hring.

Búa til ókeypis reikningFljótleg leiðarvísir

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.