## 5 Conclusions  
In conclusion, this research significantly advances information security risk assessment by integrating established standards with expert knowledge, machine learning, and ontological modeling. This approach has developed a robust foundation that enhances the understanding of the risk landscape. The use of cluster analysis and k-means on security standards uncovers hidden patterns, expanding the risk assessment scope. Additionally, machine learning enhances risk discovery and classification, while the introduction of heat maps and a thesaurus for risk descriptions innovates visualization and semantic accuracy. The development of an ontological model offers an effective, systematic method for categorizing risks. Together, these advancements significantly improve the management of information security risks, marking a major step forward in the field and setting the stage for future developments in adapting to an evolving technological environment. The model developed in this paper can be applied across various industry sectors to enhance information security risk assessment and management. The healthcare sector deals with highly sensitive data, including personal health information that must be protected from breaches and unauthorized access. The model can help healthcare organizations assess and manage risks related to data privacy and security. Banks and financial institutions are prime targets for cyber attacks due to the valuable financial and personal data they hold. The model can assist in identifying and prioritizing risks associated with online transactions, data storage, and third-party services, helping these institutions strengthen their cybersecurity measures and comply with financial regulations. As manufacturing becomes more digitized with the adoption of IoT devices and automated systems, it faces new vulnerabilities. The model can be used to assess risks associated with the integration of these technologies and to develop robust security frameworks to protect against potential cyber threats. Educational institutions store vast amounts of personal information about students and staff. The model can guide these institutions in assessing the risks associated with data storage and access, especially as e-learning platforms become more prevalent.  
Conceptualization, A.B. and A.S.; methodology, A.B.; software, A.B.; validation, A.S., G.S. and A.Z.; formal analysis, A.Z.; investigation, A.B.; resources, G.S.; data curation, A.S.; writing--original draft preparation, A.B.; writing--review and editing, A.B.; visualization, G.S.; supervision, A.S.; project administration, A.Z.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP19174390).  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.  
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.