Security Compliance for Smart Manufacturing using Knowledgegraph based Digital Twin

Abstract

The combination of Information Technology (IT) and Operational Technology (OT) in smart manufacturing, driven by smart factory innovations and Internet of Things (IoT) devices, generates vast, diverse, and rapidly evolving Big Data, which in turn increases cybersecurity and compliance issues. Adherence to security standards, such as NIST SP 800-171, which requires rigorous access control and audit reporting, is currently obstructed by the resource-intensive and error-prone aspects of manual evaluations. We have developed a semantically rich knowledge graph-based digital twin to automate security compliance of the smart assembly line, specifically focusing on categories specified in NIST SP 800-171. We have used Semantic Web technologies like RDF, OWL, and SPARQL using the Jena Fuseki server to build our system. Our approach improves data integrity and structure identification in IT/OT systems by tackling the Big Data 5Vs. The qualitative assessment of our digital twin shows a scalable approach with reduced compliance violations and enhanced audit effectiveness. In this paper, we describe our design in detail along with the validation results. This study propels future investigations by integrating Knowledge graphs and reasoning with industrial compliance, establishing a basis for automated compliance in smart manufacturing.