OpenID Connect Synthetic Data Generation for Safe and Realistic Testing

OpenID Connect (OIDC) synthetic data generation solves a common problem—testing identity flows without exposing sensitive user information. It enables developers to simulate authentication and authorization flows using fake but realistic datasets. Done right, it lets you validate tokens, scopes, claims, and client configurations with no risk of leaking real identities.

OIDC relies on a predictable set of endpoints: /authorize, /token, and /userinfo. For synthetic data generation, you emulate these endpoints with a controlled identity provider. This provider issues synthetic ID tokens, access tokens, and refresh tokens that match the schema of your real system but contain fabricated subject identifiers, email addresses, and profile claims.

Key benefits:

• Security: No link between synthetic identifiers and real users.
• Repeatable tests: Same inputs produce the same responses, helping debug flows.
• Privacy compliance: Meets regulatory requirements for data minimization.
• Integration speed: Test against a realistic OIDC environment without waiting for provisioning.

To implement:

1. Stand up a mock OIDC server or use a synthetic data generation service.
2. Define your client IDs, redirect URIs, and scopes as in production.
3. Configure token signing using disposable keys, never production private keys.
4. Populate the /userinfo endpoint with generated claims—name, email, roles—using deterministic or random generators.
5. Integrate your application’s OIDC library to point at the synthetic endpoints.

Combining OIDC with synthetic data generation lets you run integration tests, load tests, and security drills without risk. It also accelerates developer onboarding, since anyone can work against a safe identity dataset from day one.

Synthetic data in OIDC is not a toy—it is production-grade testing without the production risk.

Want to see it live? Spin up an OIDC synthetic data environment with hoop.dev and watch it running in minutes.