The Problem with Generic PII Tools
Microsoft Presidio is a solid open-source foundation for PII detection. We use it as our base. But out-of-the-box Presidio ships with approximately 30 built-in recognizers focused primarily on US formats.
When your documents contain German tax IDs, Japanese My Numbers, or Swiss AHV numbers, generic tools return empty results.
The Numbers
| Capability | Microsoft Presidio | cloak.business |
|---|---|---|
| Pattern recognizers | ~30 | 317 |
| Countries covered | Primarily US | 70+ |
| Entity types | ~20 | ~320 |
| Languages (NER) | English by default | 48 |
| Regional tax IDs | Few | 100+ |
What is Missing from Default Presidio
DACH Region (Germany, Austria, Switzerland)
| Identifier | Presidio | cloak.business |
|---|---|---|
| German Steuer-ID | Not included | Checksum validated |
| German Steuernummer | Not included | All 16 Bundesland formats |
| German Personalausweis | Not included | Full pattern |
| Austrian SVN | Not included | 10-digit format |
| Swiss AHV-Nummer | Not included | 13-digit format |
APAC Region
| Identifier | Presidio | cloak.business |
|---|---|---|
| Japanese My Number | Not included | 12-digit checksum |
| Korean RRN | Not included | 13-digit format |
| Chinese Resident ID | Not included | 18-digit with region |
| Singapore NRIC | Not included | Letter + digits + checksum |
The Consequence: Significant Accuracy Gaps
Research on multilingual PII detection found that hybrid approaches combining regex, NLP, and transformers outperform fine-tuned NER models by 82% on multilingual PII detection tasks.
For organizations operating in Germany, Japan, or other non-English markets, this is not a minor inconvenience - it is a compliance failure.
Why More Recognizers Matter
Pattern Diversity
The German Steuernummer alone has 16 different formats, one per Bundesland. Each requires its own pattern.
Checksum Validation
Pattern matching without checksum validation produces false positives. Our recognizers include validation where applicable.
Context Boosting
Detection confidence improves with context words. Our recognizers include context words in multiple languages.
The Hybrid Approach
Pattern recognizers alone are not enough. We combine three detection methods:
| Method | Strength |
|---|---|
| Regex patterns | Structured identifiers (SSN, tax IDs) |
| NLP NER | Unstructured entities (names, locations) |
| Transformer models | Context-dependent detection |
Key Takeaways
- Generic tools fail outside their training data - Hybrid approaches outperform NER by 82%
- Custom development is expensive - 1,000-2,000 hours to build comprehensive coverage
- Checksum validation prevents false positives - Pattern matching alone is not enough
- Context words must be multilingual - English context does not boost German detections
- Hybrid detection outperforms single methods - Combine regex, NER, and transformers
Sources
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