Why Regex, Not AI?
For regulatory compliance, you need results you can explain and reproduce. Our deterministic approach delivers exactly that—no black boxes, no surprises.
Detailed Comparison
| Aspect | Regex-Based (Us) | AI/ML-Based |
|---|---|---|
| Reproducibility | 100% identical results | Results may vary |
| Auditability | Fully explainable | Black box |
| Training Data | Not required | Large datasets needed |
| Model Drift | None—patterns are fixed | Degrades over time |
| Performance | Fast, predictable | ⚠️Variable, GPU-dependent |
| Compute Cost | Low (CPU only) | High (GPU often needed) |
| Regulatory Compliance | Easy to demonstrate | Difficult to prove |
How Pattern Matching Works
Each entity type has carefully crafted regex patterns that match specific formats.
Email Addresses
[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}Matches standard email format: local-part@domain.tld
Credit Card Numbers
\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|...)\bMatches Visa, Mastercard, Amex, and other card formats with Luhn validation
German IBAN
DE[0-9]{2}\s?[0-9]{4}\s?[0-9]{4}\s?[0-9]{4}\s?[0-9]{4}\s?[0-9]{2}Matches German IBAN format with optional spaces
Built for Compliance
When auditors ask "why was this detected?" you need a clear answer. Our regex-based approach provides exactly that.
- GDPR Article 25: Privacy by design with explainable processing
- ISO 27001: Documented, repeatable processes
- Audit Trail: Every detection can be traced to a specific pattern
Example Audit Response
Q: Why was "john.smith@company.com" flagged?
A: Matched email pattern at position 45-68 with confidence 0.95. Pattern: standard email format validation.
Experience Deterministic Detection
Try our regex-based PII detection free with 300 tokens per month.