Security risks and privacy challenges in digital twins
Author Identifier (ORCID)
Abstract
This chapter examines the security and privacy challenges inherent in Digital Twin (DT) systems, focusing on critical Infrastructures such as manufacturing, smart cities, and healthcare. While DTs deliver substantial benefits by offering real-time insights and optimizing operations through advanced data analytics and artificial intelligence, they also broaden the attack surface and introduce security and privacy risks. We perform a systematic analysis of vulnerabilities across the DT lifecycle and architecture layers, highlighting significant gaps in the current research landscape. To bridge these gaps, we develop security-driven lifecycle strategies, specifically Shift-Left (design stage) and Shift-Right (operational stage) approaches. The chapter also explores privacy-preserving techniques, secure architectural principles, and robust operational practices essential for safeguarding DT environments. A case study on a smart factory is presented to demonstrate real-world threats and countermeasures involving DTs. Finally, future research pathways are discussed, aiming to develop resilient, secure, and trustworthy DT ecosystems by integrating cybersecurity, privacy engineering, and governance across the lifecycle.
Keywords
digital twin security, privacy risks, cyber-physical systems
Document Type
Book Chapter
Date of Publication
6-12-2026
Publication Title
Foundations of Digital Twins
Publisher
Elsevier
School
Centre for Securing Digital Futures / School of Science
RAS ID
100038
Copyright
subscription content
Recommended Citation
Mohsin, A., Janicke, H., Holmes, D., Sarker, I. H., & Nepal, S. (2026). Security risks and privacy challenges in digital twins. In T. A. Nguyen, J. Cai, & M. Kuchaki Rafsanjani (Eds.), Foundations of digital twins (pp. 239-252). Elsevier. https://doi.org/10.1016/B978-0-443-43896-7.00016-9