The Multilingual PII Gap
The DACH region represents one of the world's largest economies with strict data protection enforcement. But most PII detection tools train models primarily on English text, lack German context words for confidence boosting, and miss region-specific identifier formats.
- NER model blindness - Models trained on English miss German entities
- Format variations - German tax IDs differ from US formats entirely
- Dialect confusion - Austrian German uses different terminology than German German
- Context word gaps - Confidence boosting only works in English
German Identifier Complexity
German-speaking regions use different identifier formats than the US. Standard NER models recognize none of these:
| Identifier | Format | Notes |
|---|---|---|
| Steuer-ID | 11 digits | German personal tax ID, checksum validated |
| Steuernummer | XX/XXX/XXXXX | Varies by Bundesland (state) |
| Personalausweisnummer | Alphanumeric | German ID card number |
| Sozialversicherungsnummer | 10 digits (Austria) | Different from German format |
| AHV-Nummer | 13 digits (Switzerland) | Swiss social insurance number |
Multi-Engine NLP Architecture
cloak.business combines three NLP engines for comprehensive coverage:
spaCy
25 languages
German, French, Spanish, Italian, Portuguese, Dutch, Polish, Russian, Japanese, Chinese, and more
Stanza NER
7 languages
Deep learning NER for additional coverage
XLM-RoBERTa
16+ languages
Cross-lingual transformer embeddings
317 Pattern Recognizers
317 Pattern Recognizers with region-specific patterns including German Steuer-ID, Austrian Sozialversicherungsnummer, Swiss AHV-Nummer, Japanese My Number, Korean RRN, and Chinese Resident ID Card.
Accuracy Improvement
| Scenario | English-Only Tools | cloak.business |
|---|---|---|
| German Steuer-ID detection | 0% (missed) | 95%+ |
| Austrian identifier detection | 0% (missed) | 95%+ |
| German name recognition | 60-70% | 90%+ |
| Japanese My Number detection | 0% (missed) | 95%+ |
Key Takeaways
- Hybrid approaches outperform NER by 82% - Combining regex, NLP, and transformers is essential
- Regional formats require specialized patterns - NER alone cannot detect structured IDs
- Context words must be multilingual - Confidence scoring only works with language-appropriate context
- 48-language support shows commitment - Not just detection, but full localization
- APAC expansion requires CJK support - Japanese, Korean, Chinese are critical markets