Cybersecurity involves diverse domains and multi-step attack chains that require compiling data from multiple sources to prevent breaches.
Attackers exploit interconnected pathways to hop between systems, while defenders often focus on static lists of permissions, creating a strategic disadvantage.
Graph databases use nodes, relationships, and labels to create a holistic view of a network, making it easier to surface hidden connections.
Learn how to load data into Neo4j using dump files or the APOC library and write basic `CREATE` and `MATCH` queries with the Cypher language.
A live demo shows how to use Cypher to trace a user's access from direct machine permissions to multi-hop group memberships.
Use Neo4j Bloom to visually explore the graph with natural language queries and apply rule-based styling with algorithms like PageRank to highlight critical assets.
Discover how to find the shortest attack path to a high-value target and visualize the full blast radius of a single compromised account.
Find resources to continue learning, including a GitHub repository with the dataset, Neo4j sandboxes, and the Graph Academy.
The speaker answers audience questions about required skills, tracking malware, and integrating machine learning with graph databases for threat detection.
43:56Mauro Verderosa
2:08:06Antonio de Mello & Amine Abed
27:32Jasmin Azemović
29:34Reto Kaeser
40:38Anna Bacher
37:32Martin Schmiedecker
1:54:28Kurt Eder
57:47Fabien Vauchelles
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Jobs that call for the skills explored in this talk.
Harvey Nash
Palo Alto Networks
CyberArk