Cybersecurity in Digital Twins and AI: An Intelligent Framework for Proactive Threat Detection and Response

Digital twins (DTs) combined with artificial intelligence (AI) are creating a big change in cybersecurity, by giving new abilities for detecting and reacting to threats in cyber-physical systems in a more proactive way. This paper introduces a detailed framework that uses AI-based digital twins for real-time security observation, anomaly detection, and prediction of possible cyber threats. The method we propose mixes machine learning techniques with digital twin systems to develop autonomous security solutions that are able to learn continuously and adapt to new situations. This integration helps to deal with important cybersecurity problems such as data accuracy, privacy protection, and the ability to grow in Industry 4.0 systems. We have tested our approach with real-world data and the results show good improvements in threat detection accuracy (99.2%) and also in reducing the response time (67%). The framework we suggest can serve as a strong basis for the future of cybersecurity, where systems can predict, model, and reduce cyber threats in real-time.