AI: The next weapon against drug cartels
Launching the ‘Nasha Mukt Yuva for Viksit Bharat Sankalp Abhiyan‘ on August 02 2026, PM Narendra Modi said the country’s youth must be mentally and physically fit, for which it is necessary for them to stay away from all kinds of substance abuse. Announcing a 100-week campaign against substance abuse, the Prime Minister asserted that the government has intensified its crackdown on drug trafficking, with arrests increasing significantly and narcotics worth thousands of crores of rupees being seized. The campaign would run every Sunday for the next 100 weeks as a nationwide ‘Jan Bhagidari’ initiative to build a drug-free India. “The coming 100 Sundays will be 100 strong steps towards a ‘drug-free India’,” the PM said. “Hostile nations conspire and leave no stone unturned to drag our youth into the trap of addiction. Profiting from the supply of drugs while aiming to destroy a country like ours is part of their terror-driven agenda,” he said.
Narcotics trafficking across international borders has been an intractable problem for all border enforcement agencies. People of various nationalities posing as tourists, students, physically challenged, airline personnel, and even diplomats, have been caught carrying narcotic drugs and substances of abuse, for delivery to drug gangs. Illegal drug production and trafficking are evolving, exploiting the darknet, social media, and unregulated online markets. For effective interdiction, law enforcement agencies need cutting-edge tools to track, analyse and dismantle drug-trafficking networks.
Technology has now advanced to the point where it is possible to detect drug trafficking by people and through freight. Rapid development of new screening technologies is coming to the aid of enforcement agencies in a big way. India also needs to explore the emerging field of Artificial Intelligence (AI) to crack down on drug trafficking gangs and networks.
Artificial intelligence is playing a growing role in predictive policing, border surveillance, and community risk mapping. Automated tools can analyse cargo movements and real-time border imagery to flag suspicious shipments or illegal entry points. Security forces can leverage data pattern recognition to trace illicit trafficking networks and pre-empt operations by organized groups. Further, government initiatives can process crowdsourced mobile reports through automated systems to deploy law enforcement resources rapidly and with extraordinary precision.
For example, the “Artificial Intelligence in fighting illicit drugs production and traffickiNg (ARIEN)” project is a unique Horizon Europe project that harnesses artificial intelligence (AI) to combat drug trafficking across the EU. It is an EU-led consortium of around 18 organisations across nine different EU countries. What makes ARIEN unique is its use of advanced AI tools to detect and track criminal activity at every stage of the drug supply chain.
The ambitious, cutting-edge research initiative is designed to help bring together law enforcement agencies, researchers and technology experts across Europe in the fight against illegal drug production and trafficking. ARIEN aims to build a real-time, intelligent picture of how drugs are produced, moved and sold across Europe. This includes:
- Monitoring financial transactions
- Spotting trends on the dark web
- Analysing how social media is being used for drug-related communication and sales
A key feature of the project is its use of explainable AI, which allows investigators to understand how the AI reaches its conclusions. This transparency helps law enforcement act on the information with greater confidence.
ARIEN will also help authorities identify new threats, uncover emerging drug markets, and spot links to organised crime by cross-referencing online activity with criminal records. Ultimately, ARIEN aims to empower law enforcement agencies, customs officers, and policymakers across the EU and beyond with smarter tools and better strategies for disrupting illegal drug networks. By fostering international cooperation and promoting responsible use of data and technology, the project is setting a new standard in the fight against drug-related crime.
The role of AI in combating drug trafficking and abuse is through three main aspects: identification, prevention, and intervention. In the identification phase, AI utilizes big data analytics and computer vision to detect and track drug trafficking networks. These technologies are capable of analysing financial transactions, spatiotemporal data, and social network information to identify trafficking patterns. In the prevention phase, AI leverages genetic analysis and technologies such as virtual reality (VR) and augmented reality (AR) to spot and identify at-risk individuals and deliver preventive educational programs. Additionally, chatbots and intelligent systems play a crucial role in reducing drug demand by providing counselling and psychological support. In the intervention phase, AI employs machine learning and AI-equipped drones to enable early detection of addiction, personalize treatment programs, and enhance border surveillance. These technologies improve the accuracy and speed of security operations, playing a key role in reducing drug production and distribution.
India also needs to launch a project like ARIEN as part of ‘Nasha Mukt Yuva for Viksit Bharat Sankalp Abhiyan’. Traditional enforcement strategies will not suffice to disrupt drug trafficking and growing drug consumption. Artificial intelligence is needed to tackle drug trafficking because it automates dark web monitoring, analyses massive financial flows, and processes digital communications faster than humanly possible. Criminal groups are also shifting to modern online tools, making advanced technology vital for law enforcement.
A popular saying sums it concisely: “Cartels exploit the sheer volume of global trade to hide their shipments. AI thrives in that exact volume, finding the needles of illicit contraband in a haystack of global commerce.”
Note:
1. Text in Blue points to additional data on the topic.
2. The views expressed here are those of the author and do not necessarily represent or reflect the views of PGurus.
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