Understanding attack and knowledge graphs
In previous chapters, we talked about attack graphs and learned how to use them to model and understand the path an adversary takes to achieve a mission objective. Take, for instance, the Bloodhound toolset (https://github.com/BloodHoundAD), which can be leveraged to scan and map out a Windows infrastructure.
But why stop there?
Why not gather more metadata in a graph database besides Active Directory information? Why not turn it into an entire knowledge graph for your organization? The graph can cloud assets such as GCP, AWS, Azure, Facebook, and Twitter, as well as vulnerability information (for instance, data from the Common Vulnerabilities and Exposure database), and so forth.
To build knowledge graphs, we can use free and commercial tools and graph systems, such as Neo4j or OrientDB. And, of course, there's the entire Apache TinkerPop ecosystem, which you should research and consider when building an enterprise knowledge...
