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Test data without real personal data

Test data should behave like real data but contain no real people. To achieve this, personal data from production datasets is masked or replaced with substitute values in a valid format – so validation checks and processes in test systems keep working.

Why production data does not belong in test systems

Personal data may only be processed for the purpose for which it was collected, or for a purpose compatible with it (Art. 6 para. 3 FADP, Art. 5 para. 1 let. b GDPR). Testing software is often not readily compatible with that purpose.

Then there is security: test environments are often less well protected, more people have access, and external developers are involved. Data protection by design (Art. 7 FADP, Art. 25 GDPR) requires such risks to be reduced from the outset with appropriate measures.

What methods are there?

Method Result Suitable for
Masking Details are made unrecognisable. Viewing documents, training
Substitute values in a valid format Realistic values; validation checks and interfaces keep working. Test systems, integration tests
Pseudonymisation Link back to the person possible with the mapping. Analyses with follow-up questions, troubleshooting
Synthetic data Generated without any link to real people. Early development, load testing

What matters with test data

  • Valid format: identifiers such as IBANs or AHV numbers pass the application's validation checks.
  • Consistency: the same person receives the same substitute value across related data.
  • Don't forget free text: names often appear in comment fields and documents.
  • Final check: check the result for any remaining unprotected details.
  • Proof: record who received which test data and for what purpose.

Sharing data for a defined purpose

What applies to test data applies to every transfer: to analytics teams, to service providers or to an AI. The principle is to share only as much personal reference as the purpose requires – and to record, for each recipient and purpose, what was shared.

Pseudonymisation and anonymisation compared

How Espresso helps

Privacy Delivery creates protected exports and test data in a valid format – and records who received what.

  • Test data

    Substitute values in a valid format – realistic, but without real people.

  • Protected exports

    Export data from a source in pseudonymised form, with a final check for any remaining unprotected details.

  • For your applications

    Via an interface with a dedicated key for each application – time-limited and revocable at any time.

Frequently asked questions

May we use production data for testing?

Only in exceptional cases, with a sound justification and protection equivalent to production. Test data without real people is the better option.

Is masked test data anonymous?

Not necessarily. If there is a route back to the person or if remaining details make them identifiable, it is still personal data.

Do validation checks in the test systems still work?

Yes, if the substitute values have a valid format. Identifiers then pass the application's checks just like real values.

Can test data be generated automatically?

Yes. Your applications can use Privacy Delivery via the interface, with dedicated keys that have only the permissions granted.

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We will show you the cockpit with sample data – and discuss where you should start.

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or write directly to info@espressodataprivacy.com