RiskOnto: An ontology-driven framework for threat modeling, risk reasoning and cyber defense knowledge integration
Year: 2026
Type: article
Abstract: In this paper, we introduce RiskOnto for Risk Ontology, an ontology-driven framework designed to augment threat modeling and risk reasoning processes with a particular focus on leveraging the NIST Cybersecurity Framework (CSF). We present a comprehensive approach built around the NIST CSF and it is designed to provide both structure and clarity in how risks are identified, assessed, and addressed which enable organizations to gain deeper insights into their threat landscape and make informed decisions regarding defensive strategies. RiskOnto 1.0 makes it possible to formally map cybersecurity risks to relevant controls, offering organizations a way to trace threats back to specific defensive actions. This allows for a more deliberate and transparent approach to managing security posture that is often difficult to achieve using traditional risk assessment methods, which tend to be fragmented or static. We detail the architecture of RiskOnto, explain how it models relationships between assets, threats, controls, and mitigation techniques, and demonstrate its application through a working prototype implementation that showcases its capabilities in realworld scenarios. The results suggest that this ontology-driven approach improves both the precision and comprehensiveness of threat modeling and risk reasoning, offering a scalable and adaptable solution for automation and explainability which are becoming increasingly important as security environments grow in complexity.
Source: Computers & Security
Institutions: University of New Brunswick, Stevens Institute of Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,100
OpenAlex ID: W7140229820