AI Test Data Generation: What Works and What Does Not

What changes the decision?

Statistical similarity, task utility and disclosure risk need separate checks. A dataset can resemble its source and still fail a downstream task. Small rare groups may be the cases that matter most. Conditioning rows and a random seed are different inputs; neither alone guarantees replay.

What does AI mean here?

A learned generator fits a model to sample data, then samples new records. Quality depends on the source, model and chosen checks. A language model can instead propose text, records or scenarios from an instruction. AI assistance can also help an engineer draft schema and rules; the engineer still reviews and runs them. These are different workflows, so a tool in one group is not automatically a candidate for the others. Tonic Fabricate: Data Agent · NVIDIA: synthetic data.

Learn, write rules or mask?

Choose learned generation when preserving useful distributions is part of the test and representative, permitted training data exists. For an exact rare business case or a deliberately invalid record, specify and test the rule explicitly. If the required relationships already exist in an approved source, transforming selected records can be useful; check the whole access and masking path. Combinations are possible, but the acceptance checks still differ.

What can go wrong?

A fixed seed controls one input; it does not by itself establish identical retraining or output across versions and environments. Rare groups can disappear even when aggregate distributions look close. Check each required constraint and business result separately. Synthetic output is not automatically anonymous: consider disclosure risk as well as access to the training data and model. SDMetrics: quality · ICO: AI and data protection.

What should the proof of concept show?

Keep a real holdout outside training. Set task metrics, subgroup checks and tolerances before fitting. Check constraints and disclosure risk separately. Record training inputs, model, software versions and sampling settings, then repeat the experiment.

• Compare useful distributions with a held-out sample and inspect rare groups.
• Test required relationships and deliberate invalid cases against expected results.
• Record source, model, version, seed, infrastructure and the scope of replay.
• Assess disclosure risk and the complete cost for the actual deployment.

Compare synthetic-data methods · Data masking guide.

Tools for this workflow

Capabilities vary by edition. Missing evidence means unknown, not a missing feature. License and available price information are in the profiles.