The growing complexity of vehicle network connectivity has broadened the cyber-threat landscape, introducing substantial safety risks for both passengers and the environment. Traditional security mechanisms, relying on rigid decision-making processes, often fail to address the demands of this dynamic and interconnected ecosystem. To effectively manage emerging security threats and adapt to diverse scenarios, integrating context awareness has become crucial. Context-aware systems can typically adapt their behaviour in response to changes in their surrounding environment using context information. Ontologies serve as powerful tools for modelling and reasoning of context information. However, existing ontology-based context-aware security models are constrained by static thresholds and fail to adapt the rapid changes in real-time. This paper introduces a dynamic context-aware real-time security model for the automotive domain. By leveraging a Python-based implementation alongside OWL 2 RL Ontology model, the proposed approach dynamically adapts context information based on live data for security analysis. The applicability and effectiveness of the proposed approach is demonstrated using a use case of EV charging process.