Bow tie diagrams have become a popular method for risk analysis and safety management. This tool describes the whole scenario of a given risk graphically, and proposes preventive and protective barriers to reduce, respectively, its occurrence and its severity. The weakness of bow tie diagrams is that they are restricted to a graphical representation of different scenarios exclusively designed by experts that ignore the dynamic aspect of real systems. Thus, constructing bow tie diagrams in an automatic and dynamic way remains a real challenge. This paper proposes a new Bayesian approach to construct bow tie diagrams from real data and improve them by adding a new numerical that enables us to implement the appropriate preventive and protective barriers in a dynamic manner. (C) 2012 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.