: Traditional Probabilistic Risk Assessments (PRAs) model dependency through deterministic relationships in fault trees and event trees, or through empirical ratio common cause failure (CCF) models. However, popular CCF models do not recognized system specific defenses against dependencies and are restricted to identical components in redundant configuration. While this has allowed prediction of system reliability with little or no data, it is a limiting factor in many applications, such as modeling the characteristics of a system design or incorporating the characteristics of failure when assessing the failure’s risk significance or degraded performance events (known as an event assessment). This paper proposes the General Dependency Model (GDM), which uses Bayesian Network to model