Come Funziona anonym.legal
Rilevamento PII deterministico basato su regex che offre risultati riproducibili al 100%. Stesso input, stesso output—ogni volta. Niente AI, niente congetture, solo corrispondenza di modelli trasparente.
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.
Perché Regex, Non AI?
Il Nostro Approccio
- Risultati riproducibili al 100%
- Completamente auditabile per la conformità
- Nessun dato di addestramento richiesto
- Decisioni trasparenti
- Prestazioni rapide e prevedibili
- Nessun drift del modello nel tempo
Approcci AI/ML
- I risultati variano tra le esecuzioni
- Decisioni in black box
- Richiede dati di addestramento
- Difficile da auditare
- Costi di calcolo più elevati
- Drift del modello nel tempo
Il Processo in 10 Passi
Dall'input all'output, ecco esattamente cosa succede al tuo documento
Testo di Input
Invia il tuo documento tramite interfaccia web, API o componente aggiuntivo di Office
Rilevamento della Lingua
Il sistema identifica la lingua del documento per un'elaborazione ottimale
Tokenizzazione
Il testo viene suddiviso in token per la corrispondenza dei modelli
Corrispondenza dei Modelli
I modelli regex esaminano oltre 50 tipi di entità
Analisi del Contesto
Il testo circostante migliora l'accuratezza del rilevamento
Punteggio di Affidabilità
Ogni rilevamento riceve un punteggio di affidabilità
Classificazione delle Entità
Gli elementi rilevati vengono categorizzati per tipo
Rivedi i Risultati
Visualizza tutti i rilevamenti con posizioni e punteggi
Applica Anonimizzazione
Scegli il tuo metodo: Sostituisci, Censura, Hash, Cripta o Maschera
Documento di Output
Scarica il tuo documento anonimizzato
MCP Server: Integrazione AI Focalizzata sulla Privacy
Come i tuoi dati fluiscono attraverso il MCP Server per mantenere sicuri gli strumenti AI
Richiesta Strumento AI
Il tuo strumento AI (Cursor, Claude) invia una richiesta contenente PII
Il Server MCP Intercetta
Il server analizza e rileva tutte le entità PII
Anonimizzazione
Le PII vengono sostituite con token o censurate
Elaborazione AI
L'AI riceve ed elabora solo dati anonimizzati
Restituzione della Risposta
La risposta dell'AI torna attraverso il Server MCP
De-tokenizzazione
Opzionale: I valori originali vengono ripristinati per l'utente
Esempio del Mondo Reale
Elaborare il pagamento per John Doe, email john@example.com, carta 4532-1111-2222-3333Cosa vede l'AI
Elaborare il pagamento per PII_PERSON_001, email PII_EMAIL_001, carta PII_CREDIT_CARD_001Cosa ottieni indietro
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.
Guarda in Azione
Prova il nostro rilevamento e anonimizzazione PII gratis con 200 token per ciclo.
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.