Human Reliability-Based MC&A Methods for Evaluating the Effectiveness of Protecting Nuclear Material

Material control and accountability (MC&A) operations that track and account for critical assets at nuclear facilities provide a key protection approach for defeating insider adversaries. MC&A activities, from monitoring to inventory measurements, provide critical information about target materials and define security elements that are useful against insider threats. However, these activities have been difficult to characterize in ways that are compatible with the path analysis methods that are used to systematically evaluate the effectiveness of a site’s protection system. The path analysis methodology focuses on a systematic, quantitative evaluation of the physical protection component of the system for potential external threats, and often calculates the probability that the physical protection system (PPS) is effective (P_E) in defeating an adversary who uses that attack pathway. In previous work, Dawson and Hester observed that many MC&A activities can be considered a type of sensor system with alarm and assessment capabilities that provide reccurring opportunities for “detecting” the status of critical items. This work has extended that characterization of MC&A activities as probabilistic sensors that are interwoven within each protection layer of the PPS. In addition, MC&A activities have similar characteristics to operator tasks performed in a nuclear power plant (NPP) in that the reliability of these activities depends significantly on human performance. Many of the procedures involve human performance in checking for anomalous conditions. Further characterization of MC&A activities as operational procedures that check the status of critical assets provides a basis for applying human reliability analysis (HRA) models and methods to determine probabilities of detection for MC&A protection elements. This paper will discuss the application of HRA methods used in nuclear power plant probabilistic risk assessments to define detection probabilities and to formulate “timely detection” for MC&A operations. This work has enabled the development of an integrated path analysis methodology in which MC&A operations can be combined with traditional sensor data in the calculation of PPS effectiveness. Explicitly incorporating MC&A operations into the existing evaluation methodology provides the basis for an effectiveness measure for insider threats, and the resulting PE calculations will provide an integrated effectiveness measure that addresses both external and insider threats. The extended path analysis methodology is being further investigated as the basis for including the PPS and MC&A activities in an integrated safeguards and security system for advanced fuel cycle facilities.