As AI adoption accelerates, rapid testing isn’t enough. You need secure, governed, production‑realistic data.

Traditional approaches that use production copies, manual masking and disconnected datasets can’t keep up. They limit scale and make it difficult to prove control and resilience.

But NIS2 demands more: active risk management, accountability and evidence of governance, especially for sensitive data in test environments.

Get it wrong, and you’re exposed to operational and regulatory risk.

Isn’t it time to change tack?

In this webinar, Conor Thomson from Resillion and Finn Lawford Mee from Synthesized.io will demonstrate how AI‑native test data automation helps you reduce that exposure, while accelerating AI assurance.

Drawing on real-world experience across both customer-facing and internal AI systems, you’ll find out how to:

• Transform sensitive production data into privacy‑safe synthetic datasets that reduce risk across non‑production environments
• Generate production‑realistic test data on demand without copying or exposing live data
• Embed repeatable, policy‑driven test data workflows that support governance, auditability, and control
• Scale AI validation, including bias testing and scenario coverage, without compromising data security
• Strengthen data quality, operational resilience, and readiness under increasing regulatory scrutiny

You’ll leave with a clear understanding of how test data automation moves from a delivery bottleneck to a critical control point helping you deliver scalable AI assurance while closing a key gap in your NIS2 readiness.