Kā anonym.legal Darbojas

Deterministiska, regex balstīta PII atklāšana, kas nodrošina 100% reproducējamus rezultātus. Tas pats ievade, tas pats izeja—katru reizi. Nav AI, nav minējumu, tikai caurspīdīga paraugu saskaņošana.

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.

Kāpēc Regex, Nevis AI?

Mūsu Pieeja

  • 100% reproducējami rezultāti
  • Pilnībā auditable atbilstībai
  • Nav nepieciešami apmācības dati
  • Caurspīdīga lēmumu pieņemšana
  • Ātra, prognozējama veiktspēja
  • Nav modeļa novirzes laika gaitā

AI/ML Pieejas

  • Rezultāti atšķiras starp izpildēm
  • Melna kaste lēmumu pieņemšanā
  • Nepieciešami apmācības dati
  • Grūti auditable
  • Augstākas aprēķinu izmaksas
  • Modeļa novirze laika gaitā

10 Soļu Process

No ievades līdz izejai, šeit ir tieši tas, kas notiek ar jūsu dokumentu

1

Ievades Teksts

Iesniedziet savu dokumentu, izmantojot tīmekļa saskarni, API vai Office paplašinājumu

2

Valodas Atklāšana

Sistēma identificē dokumenta valodu optimālai apstrādei

3

Tokenizācija

Teksts tiek sadalīts tokenos paraugu saskaņošanai

4

Paraugu Saskaņošana

Regex paraugi skenē 285+ entitāšu tipus

5

Konteksta Analīze

Aptverošais teksts uzlabo atklāšanas precizitāti

6

Uzticības Novērtēšana

Katrs atklājums saņem uzticības novērtējumu

7

Entitāšu Klasifikācija

Atklātie elementi tiek kategorizēti pēc veida

8

Pārskatīt Rezultātus

Skatiet visus atklājumus ar pozīcijām un novērtējumiem

9

Piemērot Anonimizāciju

Izvēlieties savu metodi: Aizvietot, Rediģēt, Hash, Šifrēt vai Maskēt

10

Izejas Dokuments

Lejupielādējiet savu anonimizēto dokumentu

Pieejams tikai Pro un Business plānos

MCP Serveris: Privātuma Pirmais AI Integrācija

Kā jūsu dati plūst caur MCP Serveri, lai saglabātu AI rīkus drošus

1

AI Rīka Pieprasījums

Jūsu AI rīks (Cursor, Claude) nosūta pieprasījumu, kas satur PII

2

MCP Serveris Pārtrauc

Serveris analizē un atklāj visas PII entitātes

3

Anonimizācija

PII tiek aizvietots ar tokeniem vai rediģēts

Safe data only
4

AI Apstrāde

AI saņem un apstrādā tikai anonimizētus datus

5

Atbildes Atgriešana

AI atbilde atgriežas caur MCP Serveri

6
Optional

De-tokenizācija

Opcija: Sākotnējās vērtības atjaunotas lietotājam

Reālās Pasaules Piemērs

Pirms (ar PII)
Apstrādāt maksājumu par John Doe, e-pasts john@example.com, karte 4532-1111-2222-3333

Ko AI redz

Pēc (anonimizēts)
Apstrādāt maksājumu par PII_PERSON_001, e-pasts PII_EMAIL_001, karte PII_CREDIT_CARD_001

Ko jūs saņemat atpakaļ

AI nekad neredz jūsu reālo PII
Atgriezenisks ar tokenizācijas režīmu
Tieši tādas pašas tokenu izmaksas kā tīmekļa lietotnei
Darbojas ar vairākiem AI rīkiem
Uzņēmuma līmeņa drošība

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.

Redziet To Darbībā

Mēģiniet mūsu PII atklāšanu un anonimizāciju bez maksas ar 200 tokeniem katrā ciklā.

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.