Cybernetica introduces SynthGuard, a next-generation framework designed to help organisations generate, evaluate, and share synthetic data while keeping full control over sensitive information.
As data-driven applications expand in regulated industries, organisations need secure, privacy-preserving methods for data generation and sharing. While synthetic data generation (SDG) shows promise, traditional approaches rely on centralised or external processing, creating risks around data sovereignty, ownership, and regulatory compliance. SynthGuard addresses these challenges.
SynthGuard's approach centres on decentralisation and privacy-preserving computation. The framework enables secure, modular workflows that run entirely within the data owner's control. It offers a flexible foundation with support for advanced machine-learning-based generation and rule-driven synthesis, making it suitable for sectors where trust, compliance, and reproducibility are critical.
SynthGuard has been shaped and rigorously tested through the TEADAL and LAGO initiatives, large European research efforts focused on secure cross-organisation data sharing in fields such as law enforcement, healthcare, finance, mobility, and regional planning.
Further, SynthGuard is a step towards generating testing data for complex e-government systems at the Estonian Centre of Excellence in Artificial Intelligence (EXAI).
These diverse, high-stakes environments validated SynthGuard’s ability to scale, to integrate privacy and utility evaluation directly into workflows, and to provide transparent, auditable pipeline specifications that simplify collaboration between data owners and data consumers.
SynthGuard has secured a trademark, further solidifying its position as a trusted offering. This milestone supports its ongoing development as a recognisable, reliable solution for organisations seeking a modern, compliant, and sovereign approach to synthetic data generation.
Read more: