The automotive industry undergoes significant advancements in terms of speed, processes, methods, and technology. A notable focus on autonomous driving gains widespread attention, leading various research groups to work on its development. The continuous development, especially with the introduction of digitization, expands the attack surface, emphasizing the need for enhanced vehicular security. Ongoing research efforts are dedicated to addressing and improving vehicular security in response to these challenges. Our study aims to respond to security incidents occurring during vehicle operation in real time and in real-world scenarios, with a particular focus on Electronic Control Units. We evaluate the performance of a recently proposed risk assessment scheme in terms of latency, scalability, and efficiency. Utilizing real-world datasets and randomly generated graphs with nodes ranging from 10 to 1,000,000, we assess how the performance of the scheme evolves. The study demonstrates the practical applicability of the risk assessment scheme in real time and real-world scenarios, as well as its adaptability across various hardware setups. Notably, our evaluation exceeds the average human reaction time threshold, with a response time of 0.24 seconds. Across a range of graph sizes, the average response time is 0.66 seconds. These findings validate the scheme’s applicability, offering rapid and efficient responses to security incidents.