## 4 Discussion  
The development of a flexible information security risk model is crucial in contemporary times due to the rapidly evolving landscape of information technology and the corresponding escalation of cyber threats. A new flexible information security risk model is shown in Figure 6. Cyber threats are continually evolving and becoming more sophisticated. A static or rigid information security risk model may not adequately adapt to these evolving threats. A flexible model can accommodate new threat vectors, attack methodologies, and vulnerabilities, ensuring a proactive and adaptive approach to risk assessment. Organizations operate in diverse and dynamic environments, each with its own unique set of challenges and risks. A flexible risk model can be tailored to suit specific organizational contexts, considering industry-specific regulations, business processes, technologies, and threat landscapes. This adaptability allows for a more accurate and targeted risk assessment. Legal and regulatory frameworks governing information security are continuously evolving to keep pace with the changing threat landscape. A flexible risk model can incorporate these regulatory requirements and compliance standards, assisting organizations in meeting legal obligations and minimizing legal risks. As data become a critical asset for organizations, a flexible risk model can adopt a data-centric approach, focusing on assessing risks associated with the confidentiality, integrity, and availability of sensitive information. This enables organizations to prioritize and protect their most valuable assets effectively. By providing a tailored and dynamic approach to risk assessment, a flexible risk model helps organizations allocate their resources (financial, human, and technological)efficiently. This ensures that resources are invested in areas with the highest risk impact, enhancing the overall security posture while optimizing resource utilization. The proof of flexibility for the new information security risk assessment model can be demonstrated through the iterative and multidimensional approach undertaken during its development, incorporating various methodologies and data sources. Initially, a comprehensive study of all known information security standards was conducted, and an expert list of information security risks was compiled based on this analysis. Texts of all information security standards were subjected to cluster analysis using k-means. This approach helped in identifying patterns and similarities within the standards, thus expanding the list of information security risks based on clustered findings. Machine learning methods were employed to construct an information security risk dendrogram. This approach further expanded the list of risks by uncovering relationships and hierarchies among the risks, enhancing the model's depth and coverage. A heat map of information security risks was compiled, providing a visual representation of risk relationships and intensities. Analyzing this heat map allowed for the identification and inclusion of additional risks, thus enriching the risk list. A thesaurus was compiled, incorporating definitions, synonyms, and antonyms related to information security risks. This thesaurus helped optimize the risk list by standardizing terminology and refining risk descriptions, contributing to a more precise and comprehensive assessment. An ontological model of information security risks was developed, enabling a structured and organized representation of risks. This model facilitated risk classification, providing a basis for further developing a flexible and adaptable risk assessment model. By employing a variety of analytical and data-driven techniques, including expert analysis, cluster analysis, machine learning, heat map analysis, thesaurus compilation, and ontological modeling, the information security risk assessment model was continuously enriched and refined. These iterative processes demonstrate the  
Figure 6: New flexible information security risk model.