Publication

Abstract :

With the integration of technological advances within interconnected systems, digital twins (DTs) are playing an increasingly vital role in optimizing operations, enhancing decision-making, and driving innovation across diverse industries such as manufacturing, healthcare, energy, and transportation. They offer immense potential to address one of the fastest growing concerns in today’s technological landscape, i.e., cybersecurity. This paper presents a comprehensive analysis of 300 publications, employing a mixed-method approach that includes BERTopic modeling and case studies to explore the dynamic relationship between DTs and cybersecurity. Five topics emerged—DTs in IoT environments, critical infrastructure, energy systems, AI-enhanced DTs, and sectoral efficiency—which reflect how cybersecurity capabilities are implemented across diverse industries to drive performance and resilience. Our findings include 13 case studies illustrating the real-world impact of DTs in enhancing real-time threat detection, anomaly analysis, and adaptive response. Notably, while DT applications in smart manufacturing and energy systems significantly increase operational efficiency and resilience, challenges persist in data integration, scalability, and standardization. Informed by these insights, we propose a forward-looking cybersecurity framework designed to unify cross-domain challenges and solutions observed in the literature. Unlike existing studies that remain sector bound, our framework aims to consolidate shared cybersecurity mechanisms—such as anomaly detection, AI integration, and data governance—into a coherent, actionable structure applicable across DT ecosystems. This contribution not only synthesizes fragmented research but also advances the theoretical understanding by addressing the pressing need for scalable, interoperable, and secure DT infrastructures. Furthermore, our analysis maps these advancements with UN Sustainable Development Goal 9 (SDG 9) and its subgoals SDG 9.1, SDG 9.4 and SDG 9.5. This study highlights critical gaps in intelligent data governance, standardization, and cybersecurity implementation across sectors and argues for a unified framework to guide future research and practice in secure DT development.