Radiological material transportation is primarily facilitated by heavy-duty on-road vehicles. Modern vehicles have dozens of electronic control units or ECUs, which are small, embedded computers that communicate with sensors and each other for vehicle functionality. ECUs use a standardized network architecture--Controller Area Network or CAN--which presents grave security concerns that have been exploited by researchers and hackers alike. For instance, ECUs can be impersonated by adversaries who have infiltrated an automotive CAN and disable or invoke unintended vehicle functions such as brakes, acceleration, or safety mechanisms. Further, the quality of security approaches varies wildly between manufacturers. Thus, research and development of after-market security solutions have grown remarkably in recent years. Many researchers are exploring deployable intrusion detection and prevention mechanisms using machine learning and data science techniques. However, there is a gap between developing security system algorithms and deploying prototype security appliances in-vehicle. In this paper, we, a research team at Oak Ridge National Laboratory working in this space, highlight challenges in the development pipeline, and provide techniques to standardize methodology and overcome technological hurdles.