Digital Twin (DT) technology is revolutionizing critical infrastructure (CI) sectors by enabling real-time monitoring, predictive analytics, and dynamic decision-making. However, this increased interconnectivity and complexity also introduces significant cybersecurity challenges. The current work investigates the potential of DTs to enhance cybersecurity incident detection and response mechanisms for CI systems. This investigation endeavors to explore how DTs, by leveraging machine learning and real-time data analysis, can identify cyber-specific anomalies, predict potential cyber threats, and simulate cyberattack scenarios to evaluate and optimize cybersecurity response strategies. Specifically, we emphasize the development of a comprehensive understanding of the role of DTs in enhancing system security and operational continuity, addressing evolving cyber threats with adaptive and proactive measures. Overall, our objective is to demonstrate the potential of DTs to build more resilient and secure infrastructures, safeguarding their reliability in a rapidly advancing digital landscape.