The United Nations, in their annual World Drug Report in 2018, reported that the production of Opium, Cocaine, Cannabis, etc. all observed record highs, which indicates the ever-growing demand of these drugs. Social networks of individuals associated with Drug Trafficking Organizations (DTO) have been created and studied by various research groups to capture key individuals, in order to disrupt operations of a DTO. With drug offenses increasing globally, the list of suspect individuals has also been growing over the past decade. As it takes significant amount of technical and human resources to monitor a suspect, an increasing list entails higher resource requirements on the part of law enforcement agencies. Monitoring all the suspects soon becomes an impossible task. In this paper, we present a novel methodology which ensures reduction in resources on the part of law enforcement authorities, without compromising the ability to uniquely identify a suspect, when they become “active” in drug related activities. Our approach utilizes the mathematical notion of Identifying Codes, which generates unique identification for all the nodes in a network. We find that just monitoring important individuals in the network leads to a wastage in resources and show how our approach overcomes this shortcoming. Finally, we evaluate the efficacy of our approach on real world datasets.