Organisations increasingly depend on complex, semantically rich data structures such as Knowledge Graphs (KGs) to power next-generation AI applications, including GraphRAG, neurosymbolic reasoning, and autonomous AI agents. However, strict privacy, governance, and compliance requirements significantly restrict access to these datasets, delaying innovation and slowing enterprise AI adoption. Existing privacy-preserving approaches are primarily designed for tabular data and often disrupt the relationships and semantic context that make knowledge graphs valuable.
KG Ark addresses this challenge by enabling organisations to create high-quality, privacy-preserving synthetic knowledge graphs that retain the structure, relationships, and semantic meaning of real-world data without exposing sensitive information. Acting as a "flight simulator" for enterprise AI, KG Ark allows data teams to safely develop, test, and refine AI systems in a realistic synthetic environment before deployment on live data.
This Privacy Enhancing Technology (PET) will enable enterprises to accelerate AI development cycles from months to days while reducing compliance barriers and data-access constraints. Over a 12-month TRP programme, the project will translate foundational research into KG Ark v1.0, validate the technology through a real-world enterprise pilot, and establish the technical and commercial foundations for a DCU spin-out and subsequent seed investment. The opportunity is to define a new category of privacy-first AI infrastructure supporting secure enterprise adoption of advanced AI systems.
