A staggering 450,000 people died due to drug consumption in 2015, out of which, a third of the deaths were a direct result of drug overdosing. Illicit manufacturing of Cocaine, Heroin, Cannabis, etc., by Drug Trafficking Organizations (DTOs), all peaked recently, which is a major indication of their worldwide demand. With drug offenses increasing globally, the list of suspect individuals, associated with drug trafficking organizations, has also been growing over the past few decades. As it takes significant amount of technical and human resources to monitor a suspect, an increasing list entails greater resource requirements on the part of law enforcement agencies. Soon, monitoring all the suspects on the list becomes an impossible task. In this paper, we present a novel methodology called Augmented Identifying Codes (AIC), an extension of the mathematical notion of Identifying Codes. We show that our method requires significantly lesser resources, on the part of the law enforcement agencies, when compared to strategies adopting standard network centrality measures, for monitoring of individuals associated with drug trafficking organizations. Finally, we evaluate the efficacy of our approach on real world datasets.