Videos

Speaker: Gal Engelberg, Research Associate Principal, Accenture Session type: Full Length Abstract: Today, enterprises in general and industrial manufacturers in particular are increasingly connecting to external networks. As such, industrial processes that were once isolated from the open internet network are now more vulnerable to external cyber attacks. As the frequency and resulting impact of these vulnerabilities increases, there is a need to prioritize and mitigate risks in order of importance to the business. Unlike common risk assessment tools that prioritize risks based on their potential damage to the infrastructure layer alone, we add the business context to the equation. Using Neo4j, we present a knowledge-graph-driven approach to address the above challenges. Our work will be demonstrated over a vehicle assembly smart manufacturing environment. First, we present the notion of process-aware attack-graphs: a semantic representation of the factory infrastructure and industrial-process layers. We base the approach on the usage of graph data science algorithms to quantify the cybersecurity risk based on potential adversary behaviors. Then, map the risk from the infrastructure layer to the process layer. And lastly, to identify the risk root cause and recommend, which issues to address first accordingly. This session will be focused on the usage of Neo4j Graph Data Science algorithms over knowledge graphs while triaging business and cybersecurity.