The purpose of risk assessment is to determine the levels of risk in a particular course of action, and it is usually used to identify areas in which safety can be improved, and its importance has been widely recognized and accepted. A Bayesian network is a carrier of the conditional independencies of a set of variables, and it is based on the Bayes’ theorem, and it provides an efficient way to model the risks with their cause and effect relationships. In this thesis, we have proposed a tool called RAT in order to assist the risk assessment activities, and it takes advantage by providing a user friendly GUI and facilitates users to establish and experiment Bayesian networks. In addition, users can spot the differences between each node of a Bayesian network at different time, and it helps to have a better understanding on the trend of a node’s risk level from time to time, and it can act as information assistant while revising the network. The tool can save a large amount of calculation time and provides a more efficient way to assess the risks.