anonym.legal

Kuidas anonym.legal töötab

Deterministlik, regex-põhine PII tuvastamine, mis tagab 100% korduvad tulemused. Sama sisend, sama väljund—igal korral. Ei mingit AI-d, ei mingit oletamist, vaid läbipaistev mustri sobitamine.

Proovige tasutaTehniline dokumentatsioon

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.

Miks Regex, mitte AI?

Meie lähenemine

  • 100% korduvad tulemused
  • Täielikult auditeeritav vastavuse jaoks
  • Ei ole vajalik koolitusandmeid
  • Läbipaistev otsuste tegemine
  • Kiire, ettearvatav jõudlus
  • Ei mudeleid aja jooksul

AI/ML lähenemised

  • Tulemused varieeruvad jooksude vahel
  • Must kast otsuste tegemine
  • Nõuab koolitusandmeid
  • Raske auditeerida
  • Kõrgem arvutusmaksumus
  • Mudelite nihkumine aja jooksul
Tutvuge meie tehnoloogiaga

10-sammuline protsess

Sisendist väljundini, siin on täpselt see, mis teie dokumendiga juhtub

1

Sisendtekst

Esitage oma dokument veebiliidese, API või Office lisandmooduli kaudu

2

Keele tuvastamine

Süsteem tuvastab dokumendi keele optimaalseks töötlemiseks

3

Tokeniseerimine

Tekst jagatakse tokeniteks mustri sobitamiseks

4

Mustri sobitamine

Regex mustrid skaneerivad 285+ üksuse tüüpi

5

Konteksti analüüs

Ümbritsev tekst parandab tuvastamise täpsust

6

Usaldusväärsuse hindamine

Iga tuvastamine saab usaldusväärsuse skoori

7

Üksuse klassifitseerimine

Tuvastatud esemed klassifitseeritakse tüübi järgi

8

Tulemuste ülevaatus

Vaadake kõiki tuvastamisi koos positsioonide ja skooridega

9

Rakenda anonüümitus

Valige oma meetod: Asenda, Redigeeri, Hash, Krüpteeri või Maskeeri

10

Väljunddokument

Laadige alla oma anonüümitud dokument

Saadaval ainult Pro ja Business plaanides

MCP Server: Privaatsus-esimene AI integreerimine

Kuidas teie andmed voolavad MCP Serveri kaudu, et hoida AI tööriistad turvalisena

1

AI tööriista päring

Teie AI tööriist (Cursor, Claude) saadab päringu, mis sisaldab PII-d

2

MCP Server peatab

Server analüüsib ja tuvastab kõik PII üksused

3

Anonüümitus

PII asendatakse tokenitega või redigeeritakse

Safe data only
4

AI töötlemine

AI saab ja töötleb ainult anonüümseid andmeid

5

Vastuse tagastamine

AI vastus tuleb tagasi MCP Serveri kaudu

6
Optional

De-tokeniseerimine

Valikuline: Algväärtused taastatakse kasutajale

Reaalmaailma näide

Enne (PII-ga)
Tehke makse John Doe eest, e-post john@example.com, kaart 4532-1111-2222-3333

Mida AI näeb

Pärast (anonüümitud)
Tehke makse PII_PERSON_001 eest, e-post PII_EMAIL_001, kaart PII_CREDIT_CARD_001

Mida te tagasi saate

AI ei näe kunagi teie tegelikku PII-d
Tagasivõetav tokeniseerimise režiimiga
Samad tokeni kulud kui veebirakendusel
Toimib mitme AI tööriistaga
Ettevõtte tasemel turvalisus
Tutvuge MCP Serveriga →

Uurige edasi

Tehnoloogia

Süvitsi regex-põhisesse tuvastamisse ja miks see on parem vastavuse jaoks

Arhitektuur

Süsteemi arhitektuur ja kuidas komponendid koos töötavad

Turvalisus

Viis turvakihti, mis kaitsevad teie andmeid igas etapis

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.

Vaadake seda tegevuses

Proovige meie PII tuvastamist ja anonüümitust tasuta 200 tokeniga iga tsükli kohta.

Looge tasuta kontoKiire algusjuhend

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.