Assessing risk in a computational grid environment is an essential need for a user who runs applications from a remote machine on the grid, where resource sharing is the main concern. For correctly predicting the risk environment, we made a comparative analysis of various machine learning modeling methods on a dataset of risk factors. First, we conducted a survey with International experts about the various risk factors associated with grid computing. Second, we assigned numerical ranges to each risk factor based on a generic grid environment. We utilized data mining tools to pick the contributing attributes that improve the quality of the risk assessment prediction process.