Production-shaped data. PII-safe by default.
Mark fields as sensitive in your DataMaker template and the agent guarantees they're masked, replaced, or never exported. GDPR-friendly. Audit-ready.
Brittle anonymisation slows everything down.
Hand-rolled scripts drift
Every team writes its own anonymiser. They miss new PII fields, fail audits, and break with each schema change.
Production copies are a liability
A snapshot of prod for testing is a GDPR incident waiting to happen, even with a 'safe' subset.
Synthetic data is too unrealistic
Faker gives you 'John Doe' rows that don't trigger the bugs real customers hit. Tests pass; production doesn't.
Masked at the field. Tracked at the schema.
Sensitive fields are first-class. Templates declare them; the agent honours them.
Mark any field sensitive
Add a flag in the template. Exports are blocked unless explicitly approved, and the agent refuses to copy the value.
Realistic without being real
Generate IBANs that pass checksum, USt-IDs that match country format, addresses that match postcodes.
Same shape, different identity
Email "alice@…" → "fcbb@…". UUIDs stay UUIDs. Dates stay dates. Tests behave the same way.
Refuses unsafe exports
The AI agent will not copy sensitive fields out of templates without explicit instruction, by design.
Mark a field. Generation is masked.
DataMaker substitutes a realistic, locale-correct fake at generation time and refuses to export the underlying value.
"Lukas""lukas.huber@example.de""Müller GmbH""DE89 3704 0044 …"1485.20- GDPR-compliant by default
- Mask, replace, or block-on-export
- Audit log per template
- Locale-aware substitutions
Our DPO signed off our DataMaker rollout in a single review. The sensitive-flag at the template level was the clincher.
Realistic test data without the GDPR risk.
Try the masking workflow on the free tier, no production data needed.