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Synthetic DPL Data in AIQ

Use Synthetic DPL data when AIQ tests need generated values that are safe, repeatable enough for validation, and independent from production customer records. Synthetic data should match the format and constraints of the application under test.

Good use cases

  • Generating names, IDs, addresses, emails, or other non-sensitive values.
  • Creating unique values per run to avoid duplicate-data conflicts.
  • Testing validation rules without exposing real customer data.
  • Supporting performance or regression runs that need large data volume.

Quality checks

  1. Confirm generated values match field length, format, and business rules.
  2. Document randomness, uniqueness, and cleanup expectations.
  3. Do not use synthetic values that accidentally resemble real sensitive data.
  4. Validate failures against both scenario logic and generated data quality.

Last updated: October 6, 2026

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