Enhancing Cybersecurity in Digital Twin Systems: Mitigating Challenges and Defending Against Threats

Digital Twins (DTs) are precise copies of actual systems that exist to which people can interact dynamically. Employed mainly in manufacturing, healthcare and smart city sectors, DTs facilitate the improvement of performance and decision-making. This integration raises the risk of cybersecurity threats including loss of data; wrong access; and advanced persistent threats. Since through integrating DT many other infrastructures are attained by the attackers it becomes a very significant agenda to counter them. In this paper, the strategy of using AI and XAI to enhance cybersecurity in DT systems is analyzed. Some of the AI techniques are Anomaly detection, analysis that predicts and Automated response facilities to provide Security precautionary measures. Such methods are improved by XAI through the addition of the process by which the decisions were made and the trust with the parties involved. Mirroring literature review, deficits of existing DT cybersecurity frameworks, analyzed, include overall issues of scalability, insufficient new threat integration, and increased opacity. For such reasons, the current paper offers a detailed cybersecurity framework that adopts both AI and XAI to fill the existing research gaps. The framework has demonstrated the application of improvements in threat detection, downloads response time and system robustness using real life cases. The presented work is intended for those states where it is possible to create safe and secure DT environments, efficient and capable of countering modern cybersecurity threats. They are aimed to cover existing voids in order to create a socially responsible approach to the use of DT in sensitive domains.