Paano Gumagana ang anonym.legal
Deterministic, regex-based PII detection na nagbibigay ng 100% na maulit na mga resulta. Parehong input, parehong output—tuwing oras. Walang AI, walang hula, tanging malinaw na pattern matching.
How Does PII Detection Work?
PII detection identifies personal data in text using pattern matching and machine learning. anonym.legal uses a hybrid approach:
- 1Pattern Matching: Regex patterns detect structured data (SSNs, credit cards, IBANs) with checksum validation.
- 2Named Entity Recognition: NER models identify names, locations, and organizations in 48 languages.
- 3Context 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.
Bakit Regex, Hindi AI?
Aming Pamamaraan
- 100% na maulit na mga resulta
- Ganap na ma-audit para sa pagsunod
- Walang kinakailangang training data
- Malinaw na paggawa ng desisyon
- Mabilis, predictable na pagganap
- Walang model drift sa paglipas ng panahon
AI/ML na Pamamaraan
- Nag-iiba-iba ang mga resulta sa bawat takbo
- Black box na paggawa ng desisyon
- Kailangan ng training data
- Mahirap i-audit
- Mas mataas na gastos sa compute
- Model drift sa paglipas ng panahon
Ang 10-Hakbang na Proseso
Mula sa input hanggang output, narito ang eksaktong nangyayari sa iyong dokumento
Input na Teksto
I-submit ang iyong dokumento sa pamamagitan ng web interface, API, o Office Add-in
Pagtukoy ng Wika
Tinutukoy ng sistema ang wika ng dokumento para sa pinakamainam na pagproseso
Tokenization
Ang teksto ay hinahati sa mga token para sa pattern matching
Pattern Matching
Ang mga regex pattern ay nag-scan para sa higit sa 50 uri ng entity
Pagsusuri ng Konteksto
Ang nakapaligid na teksto ay nagpapabuti sa katumpakan ng pagtukoy
Pagsusuri ng Kumpiyansa
Bawat pagtukoy ay tumatanggap ng kumpiyansa na marka
Pag-uuri ng Entity
Ang mga natukoy na item ay ikinategorya ayon sa uri
Suriin ang mga Resulta
Tingnan ang lahat ng pagtukoy kasama ang mga posisyon at marka
Mag-apply ng Anonymization
Pumili ng iyong pamamaraan: Palitan, Redact, Hash, Encrypt, o Mask
Output na Dokumento
I-download ang iyong anonymized na dokumento
MCP Server: Privacy-First AI Integration
Paano dumadaloy ang iyong data sa MCP Server upang mapanatiling ligtas ang mga AI tool
Kahilingan ng AI Tool
Ang iyong AI tool (Cursor, Claude) ay nagpapadala ng kahilingan na naglalaman ng PII
MCP Server ang Humahadlang
Sinusuri ng server at tinutukoy ang lahat ng PII entities
Anonymization
Ang PII ay pinapalitan ng mga token o redacted
AI Processing
Tumatanggap ang AI at nagpoproseso lamang ng anonymized na data
Pagbabalik ng Tugon
Ang tugon ng AI ay bumabalik sa pamamagitan ng MCP Server
De-tokenization
Opsyonal: Ang mga orihinal na halaga ay ibinabalik para sa gumagamit
Tunay na Halimbawa
Iproseso ang pagbabayad para kay John Doe, email john@example.com, card 4532-1111-2222-3333Ano ang nakikita ng AI
Iproseso ang pagbabayad para sa PII_PERSON_001, email PII_EMAIL_001, card PII_CREDIT_CARD_001Ano ang makukuha mong pabalik
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.
Tingnan Ito sa Aksyon
Subukan ang aming PII detection at anonymization ng libre na may 200 tokens bawat cycle.
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
- Common questions
- Glossary
- How tokens work
- Security posture
- Where we comply
- What we detect
- Case studies
- Release notes
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
- Open the web app and try a sample file.
- Learn how credits get counted.
- See current plans and limits.
- Meet the team behind the product.
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.