Improving Critical Infrastructure Protection: Assessment Methods, AI, Digital Twins

This article analyzes modern methods of protecting critical infrastructure (CI) in the face of growing cyber threats, focusing on the use of artificial intelligence (AI), digital twins, and other innovative technologies. Traditional approaches to ensuring security are discussed, including the use of physical and technical means, such as firewalls and intrusion detection systems (IDS). It is noted that although these methods remain important, they are becoming insufficient to protect against increasingly complex attacks. Particular attention is paid to the role of AI and machine learning in enhancing cyber defense. These technologies allow analyzing large amounts of data, identifying anomalies, and predicting threats in real time, which significantly increases the accuracy and speed of incident response. The concept of digital twins is also presented, which create virtual copies of CI objects, allowing threat modeling and prompt response to potential risks. The authors propose methods for quantitative assessment of cybersecurity, including the Harrington desirability function and the analytic hierarchy process, which allows taking into account both quantitative and qualitative aspects of protection. Examples of successful application of these approaches in energy, transport, healthcare and other industries are given, and prospects for their further use are described. The conclusion emphasizes the need to develop AI and digital twin technologies to improve the sustainability and security of CI. The article demonstrates the importance of an integrated approach to assessment and protection, focusing on the development of new models and methods to counter modern threats.