A Digital Twin simulation model is a powerful tool for implementing advanced analytics to support process optimization, predictive failure analysis, and optimally scheduled maintenance. The unique machine learning software and computational demands of a modern digital twin simulation for complex machines typically require a cloud hosted model. This presents a challenge for industrial application owners who are concerned about protecting their operations technology (OT) network from cybersecurity threats. This talk will look at the unique data flows and special security properties of a digital twin deployment for industrial equipment. The DHS and NIST guidelines will be used to develop a secure operations model that meets the unique demands of industrial control systems. The resulting model will be used to suggest a set of recommended best practices for an integrated, defense-in-depth strategy security strategy for digital twin analytics.
Learning Objectives:- Overview of digital twin architectures and security implications.
- Review of the DHS and NIST guidelines for ICS networks.
- Recommended best practices for digital twin applications that rely on hosted analytics services.
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