AI on the frontlines: Transforming border security in the fight against drug and arms smuggling

Abstract

Transnational drug and arms smuggling has evolved into a multidimensional challenge, increasingly enabled by advanced technologies that outpace traditional border security systems. This review offers a comprehensive and original synthesis of how Artificial Intelligence (AI) is reshaping global counter-smuggling efforts across land, maritime, and cyber domains. Unlike prior literature that often focuses on narrow technical or regional applications, this review uniquely integrates interdisciplinary perspectives, drawing from computer science, international security, ethics, and policy. It distinguishes itself by exploring a wide spectrum of cutting-edge AI tools, such as federated learning, quantum machine learning, swarm robotics, and explainable AI, within real-world border control operations. The review also addresses adversarial AI threats rarely examined in depth, including deep-fake documentation and subterranean drone deployments. These forward-looking insights set the review apart as both a technological analysis and a strategic foresight exercise. Furthermore, this work critically evaluates the ethical, legal, and infrastructural challenges of AI deployment, particularly in low- and middle-income countries, an angle largely neglected in existing literature. It highlights disparities in technological access, the risks of algorithmic bias, and the tensions between national security and individual privacy, offering concrete mitigation strategies such as decentralized machine learning models. Ultimately, this review is distinctive for its depth, breadth, and policy relevance through comparative case studies. It goes beyond technical functionality to underscore the ethical and geopolitical implications of AI in border security, positioning technology not only as a powerful defense tool but also as a strategic domain requiring global cooperation, responsible governance, and sustained innovation.