Networks including Cloud are becoming more and more ubiquitous and the number of entities/nodes they have is increasing rapidly. This situation looks grim from a security point of view since the risks for most of the devices in these networks are unknown. In this project, we focus on developing a system that estimates the risks on entities over time and we present methodologies to use these estimates for managing the risks in the network. We develop a graphical model using the connections made in this network that can incorporate side information such as risks of limited number of entities. This graphical model is natural to the problem and an effective way to learn the relationships among entities present. Then we use the graph to propagate the risks where given initial risk estimates on every entity we predict these estimates over time.