Systems for threat analysis enable users to understand the nature and behavior of threats and to undertake a deeper analysis for detailed exploration of threat profile and risk estimation. Models for threat analysis require significant resources to be developed and are often relevant to limited application tasks. This paper investigated the implicit and explicit uncertainty assessments to be taken into account for threat analysis systems to be effective for providing a relevant threat characterization. The intent of this paper is twofold. The first is to present and discuss an approach to define a model for cyber threats within a simplified expert model and to translate it into a Bayesian network as a tool for the development of practical scenarios for cyber threats analysis. The second is to address the question of assessing the Bayesian network build and its intrinsic knowledge representation model and to show how modeling decisions impact the outcome of the system. The paper describes the construction of an expert model and the corresponding BN to analyze cyber threats, investigates various types of induced uncertainty with the URREF criteria simplicity and expressiveness and implements an assessment procedure to evaluate the overall approach.