Knowledge-Augmented Security Risk Identification for OT Container Deployments

abstract

Container deployments in operational technology (OT) environments pose unique security challenges, especially when privileged configurations are used. Traditional risk identification methods often fall short in addressing the complexity, dynamic nature, and interdisciplinary collaboration required in these settings. We propose a knowledge augmentation approach that combines semantic modelling, automated reasoning, and tool support to enhance security risk identification. Our approach is demonstrated through an industrial case study, highlighting its practical application. We also examine how large language models (LLMs) can support the instantiation and integration of the approach, improving usability and scalability.