Synthetic Data for GDPR Compliance
The Regulation
GDPR — Article 25 (Data Protection by Design)
Status: In force since May 2018
Requirement: Data protection by design and by default. Organizations must implement technical measures to minimize personal data processing.
Who’s affected: Any organization processing personal data of EU residents — banks, insurers, fintechs, payment processors, wealth managers
Penalty for non-compliance: Up to €20 million or 4% of global annual turnover
The Data Challenge
GDPR creates a specific challenge for data teams: you need realistic data to test, train, and validate your systems — but the data you need is exactly the data the regulation protects.
The traditional approach is anonymization — take production data, strip identifying fields, and use the result for testing. But anonymization creates its own compliance risks:
- GDPR applies during the anonymization process itself (it’s a processing operation)
- Re-identification risk persists for rich datasets with many attributes
- Legal review is required for every data extraction project
- Field correlations degrade when anonymization is thorough enough to be effective
How Born-Synthetic Data Solves This
Born-Synthetic data eliminates personal data from testing and training entirely. No anonymization needed, no re-identification risk, no GDPR processing obligations. Data protection by design is achieved by construction — not by bolting privacy controls onto existing personal data.
What Born-Synthetic means
Born-Synthetic data is generated entirely from mathematical distributions and cultural models. No real customer data is used as input at any stage. Every profile is synthetic from birth — there is no “original” to trace back to, no lineage to real individuals, and no GDPR processing obligations.
What you get
- 29 interlocked compliance fields per profile (KYC/AML Enhanced) or 19 financial fields (UHNWI)
- 6 geographic niches with culturally accurate profiles — Silicon Valley, Old Money Europe, Middle East, LatAm, Pacific Rim, Swiss-Singapore
- Statistically valid distributions — Pareto curves for wealth, correlated risk fields, algebraically balanced balance sheets
- Certificate of Sovereign Origin — full provenance documentation for regulatory audits
- Zero balance sheet errors — verified by DIAMOND Standard audit
Pricing
| Package | Records | Fields | Price |
|---|---|---|---|
| Compliance Starter | 1,000 | 29 | $999 |
| Compliance Pro | 10,000 | 29 | $4,999 |
| Enterprise | 100,000 | 29 | $24,999 |
UHNWI packages (19 fields) start at $499 for 1,000 records.
Try Before You Buy
Download a free 100-record sample — all fields, full Certificate of Sovereign Origin, no registration required.
Not sure if your current data practices create GDPR compliance risk?
VIEW THE FULL COMPLIANCE TIMELINE →
Q: Does born-synthetic data satisfy GDPR requirements?
A: Born-Synthetic data addresses the data governance and testing requirements of GDPR by providing realistic, compliant-by-construction datasets with full provenance documentation. It eliminates the privacy risks of using production data while maintaining the statistical validity needed for meaningful testing.
Q: How is born-synthetic data different from anonymized data for GDPR compliance?
A: Anonymized data starts from real records and carries residual re-identification risk. Born-Synthetic data starts from mathematical distributions — no real person’s data is ever input or processed. This distinction provides clearer regulatory standing and eliminates the privacy-utility tradeoff inherent in anonymization.
Q: What documentation does born-synthetic data provide for GDPR audits?
A: Every dataset ships with a Certificate of Sovereign Origin documenting the generation methodology, statistical distributions used, integrity audit results (zero balance sheet errors), and provenance chain confirming no real data was used. This documentation is designed to satisfy regulatory audit requirements.
