## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Description** & **Role in Risk Assessment** & **Impact on Model Flexibility** \\ \hline \multirow{4}{*}{Machine learning} & Utilizes algorithms to identify & Enhances predictive accuracy & \multirow{4}{*}{Allows dynamic adaptation to new data and trends.} \\  & patterns and make predictions based & and identifies & \\  & on data. & emerging threats. & \\ \multirow{4}{*}{Ontological model} & A structured representation defining & Facilitates consistent and & \multirow{4}{*}{Enables scalability and \multirow{4}{*}{integration of new  risk factors.}} \\  & the types and interrelationships of & comprehensive risk & \\  & risk factors. & categorization. & risk categories. \\ \multirow{4}{*}{Thesaurus} & A repository of terms and their & Improves the precision of risk & \multirow{4}{*}{Enhances adaptability by \multirow{4}{*}{ensourcing  risk factors.}} \\  & synonyms used in risk descriptions to & descriptions and aids in & \\  & ensure consistency. & standardized communication. & terminologies. \\ \multirow{4}{*}{Heat maps} & Visual tools that display risk intensity & Aids in visualizing and & \multirow{4}{*}{Improves decision-making  efficiency by highlighting  critical areas  needing attention.} \\  & across different areas, facilitating & prioritizing risks based on  severity and & \\  & easier interpretation of data. & & \\  & Tree-like diagrams that show the & Helps in understanding the & \multirow{4}{*}{Supports detailed analysis  and refinement of risk clusters  for better risk segmentation.} \\ \multirow{2}{*}{Dendrogram} & Tree-like diagrams that show the & Helps in understanding the & \multirow{2}{*}{Supports detailed analysis  and refinement of risk clusters  for better risk segmentation.} \\  & arrangement of clustered data based & hierarchical relationships & \\  & on similarity levels. & among various risk factors. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Features and components of the proposed risk model. expansion is essential to accurately reflect the dynamic and evolving nature of cyber threats, which traditional datasets may fail to capture adequately. In subsequent research, leveraging the established ontology of information security risks, we aim to develop a fuzzy expert system tailored for assessing the information security posture of enterprises. This initiative is anticipated to mitigate the integration complexities associated with machine learning, multi-layer clustering, and ontological models. We hypothesize that the introduction of a fuzzy expert system will address the implementation challenges currently faced by small and medium-sized enterprises (SMEs) or organizations lacking extensive technical resources. Moreover, this system is expected to streamline both the implementation and maintenance processes, thereby enhancing usability and accessibility in diverse organizational contexts. Although ethical considerations are often not extensively addressed in technical cyber-security research, in future studies, we will contemplate the implications associated with data privacy during the processing of large datasets of organizational risk data. This consideration is critical to ensuring compliance with the evolving data protection regulations and maintaining stakeholder trust in cybersecurity practices.