With the aim to monitor, prevent, and predict cyber attacks on various systems and infrastructures, the cyber defence company needed a solution to ingest and connect all available data and discover threat patterns. GraphAware Hume powered by Neo4j provided a solution which merges siloed data from varied sources, then processes and connects it all in a single knowledge graph. The knowledge graph allows intelligence analysts to investigate and track patterns of threats - both real and potential, and collaborate on a single canvas.
GraphAware’s client is a cyber defence agency dedicated to protecting networks by combining open-source and commercially available intelligence to model relevant threat landscapes. The identification of incoming attacks, their origins and targets is their core competency. The company’s teams home in on attack vectors and share the attack details with stakeholders. Additionally, they analyse the collected data to identify and disrupt misinformation campaigns. The teams had extensive experience with available tools and were certain that a knowledge graph was the ideal solution for their mission-critical business challenge.
The client was facing two major challenges: Chiefly, to collect all available data into one collaborative tool from numerous sources they held as well as new streams of incoming data. The initial goal was to have a single tool to connect, structure, and match data, so that information can be shared among the cyber security teams, the attacked organisation or infrastructure, and information intelligence teams.
Secondly, the team needed…