Synthetic test data with AI: beyond the data bottleneck

Copying production data to a test environment is fast, but risky. Writing fixtures by hand is safe, but slow. Synthetic data promises a third way.

Realistic test data has always been a compromise, and AI-generated synthetic data promises to change that: realistic enough to test meaningfully, without a single real person in the dataset.

Why production data isn't a neutral choice

How synthetic generation works

A model learns the statistical properties of a dataset (distributions, correlations between fields, realistic value ranges) without the goal being to reproduce existing records or retain individual people. The result: new, plausible records that behave like real data, without a traceable person behind them.

Synthetic data still needs to be validated: it should not only statistically resemble production data, but also support the business rules you want to test.

Where most of the value lies

• Filling test environments without waiting for a data export. No more approval process for every test dataset.
• Deliberately constructing edge cases. Combinations of values that rarely occur in production, but still need to be tested.
• Load and performance testing at scale. Generating large volumes of realistic data without straining a production database.

For some tests, production insight remains important. Think of migrations, reporting, or complex data quality checks where the genuine exceptions are exactly what's relevant. Synthetic data is strongest when you need controlled, scalable and safe test datasets.

What to stay alert for

Not every "synthetic" label is equally solid. Some tools simply generate masked production data: replacing names with random text, with the underlying structure and sometimes even traceable patterns intact. Ask further: is a completely new statistical model being trained, or are existing records merely obscured? That difference determines whether you're actually avoiding a privacy risk.

An example from practice

At an insurer, the test team wanted to run load tests with a hundred thousand policies, but exporting production data required a two-week approval process through the DPO every time. With a synthetic data model trained on the statistical properties of an anonymised sample, they generated a hundred thousand new, plausible policies themselves within an hour.

One step that's often skipped: the test team had an actuary validate the synthetic dataset before it was used, to confirm the generated claim patterns were actually realistic and not just statistically plausible.

What we watch for with a synthetic data model

• Have a domain expert validate the output, not just the test team. Statistically plausible isn't the same as substantively correct.
• Test explicitly for re-identifiability: could a generated record, combined with other sources, still be traced back to an individual?
• Keep the distinction between synthetic and masked clear: explicitly ask a vendor whether a new model was trained, or existing fields were merely substituted.