A security analytics approach that exploits the unique strengths of Bayesian networks, machine learning and rules-based systems leads to powerful solutions that are effective across a wide array of security missions. The ideal security analytics solution must be built explicitly for (and with input from) end users, such as insider threat analysts in a security operations center (SOC) or those assessing highly cleared government personnel. Building a Bayesian network is a time-consuming and expensive process, but it can be dramatically simplified by first identifying the important problem concepts, then specifying the qualitative relationships between the concepts and finally using software to assemble the qualitative knowledge into a quantitative model.”]