Charles River Analytics developed prediction and sensitivity analysis tools and algorithms under the Intelligence Advanced Research Projects Activity (IARPA)’s SCITE program. IARPA sought methods that allow analysts to forecast the accuracy and sensitivity of different insider threat detection systems in large enterprise organizations.
Charles River led a team under the Probabilistic Relational Inference Modeling for Enterprises (PRIME) effort. The effort resulted in probabilistic models of enterprise-deployed machine learning systems (such as insider threat detection systems).
Analysts can use these models to evaluate, forecast, and understand the performance of a machine learning system within the enterprise.