In the context of rapidly increasing cyber threats and the growing complexity of corporate IT infrastructure, ensuring a reliable and proactive approach to cybersecurity is becoming critically important for organizations of all sizes. Traditional cybersecurity assessment methods often fail to keep up with the dynamic nature of emerging threats – necessitating the development of more adaptive and intelligent evaluation systems. This article presents a comprehensive modular system for assessing the cybersecurity level of corporate networks – offering a holistic view of the security landscape by integrating both technical and organizational indicators. The proposed system utilizes self-organizing analytical methods to dynamically process large volumes of data related to vulnerabilities, configuration states, and network behavior patterns. Through intelligent algorithms and adaptive learning, the system is capable of autonomously detecting anomalies, evaluating potential attack vectors, and correlating threats with the network’s weak points. Additionally, the inclusion of organizational factors – such as policy compliance, user behavior, and access structures – enables a more contextual and in-depth risk assessment. A key advantage of the system is its ability to perform real-time monitoring and dynamic risk evaluation – empowering decision-makers to take informed actions in response to incidents. The system's architecture supports scalability and compatibility with existing security tools and network management platforms. To validate its effectiveness, the system was implemented and tested in a simulated corporate environment reflecting modern structural and operational challenges. The experimental results confirmed its capability to identify vulnerabilities, prioritize responses, and enhance overall cyber resilience. This research contributes to the advancement of next-generation cybersecurity assessment tools – ensuring the continuous improvement of corporate defense mechanisms in an ever-changing cyber landscape.
RAMSKYI et al. (Thu,) studied this question.