A multinational cybersecurity and defense company, which integrated large volumes of data from various vendors wanted to be able to easily navigate and analyze the vast and constantly changing flow of information. This included data on security threats (malware, adware, spyware, ransomware, etc.), infected software downloads, OS releases and updates, software vulnerabilities, application releases and updates, security patches, etc.
For example, the moment a new virus appears, the company needs to be able to quickly determine which software/hardware combinations of a product (and the associated internal systems) will be affected by and vulnerable to this virus and all its variants, and immediately advise their corporate clients.
The required solution had to be able to leverage both structured (from public and proprietary datasets) and unstructured data (from textual sources). Therefore, some of the main challenges were:
After trying some other products, the cybersecurity company chose GraphDB for its ability to handle massive load, querying, and inferencing in real time.
Powered by Ontotext’s RDF database for knowledge graphs, the company was able to represent their clients’ security infrastructure and all available cyber threat intelligence in a semantic model. This model captured the “meaning” of the constantly growing cybersecurity and product data with all its inherent relationships in a single graph that evolved with each new fact.
The live cybersecurity graph provided unified access to knowledge from multiple sources and translated the huge volumes of data into valuable information. Thanks to GraphDB’s inference capabilities, now the company can easily discover relevant cybersecurity information about each new security alert or OS release/update/patch and use it for making quick decisions.
With Ontotext’s leading semantic graph database, now the company can easily navigate and analyze information to quickly identify risks and advise their clients.
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