AGENTSHIELD: AN AGENTIC ARTIFICIAL INTELLIGENCE FRAMEWORK FOR REAL-TIME CYBERSECURITY ORCHESTRATION IN INDIA’S UPI AND FINTECH ECOSYSTEM

Abstract

India's Unified Payments Interface (UPI) has emerged as the world's largest real-time payment ecosystem, processing over 13.9 billion transactions worth USD 230 billion in a single month as of 2024. This rapid digitization of financial transactions has simultaneously expanded the cyber-threat landscape targeting India's financial technology sector, with reported financial cyber-crimes rising by 334% between 2019 and 2024. Existing security frameworks — including rule-based fraud detection, static anomaly scoring, and conventional machine learning models — demonstrate significant latency and precision limitations against sophisticated, polymorphic attack vectors such as SIM-swap fraud, deep fake-enabled social engineering, and API injection attacks on payment gateways. This paper proposes Agent Shield, a novel Agentic Artificial Intelligence framework for real-time, autonomous cybersecurity orchestration across UPI and broader FinTech ecosystems. Agent Shield deploys a multi-agent architecture comprising a Threat Intelligence Agent, a Transaction Anomaly Agent, a Behavioral Biometrics Agent, and a Regulatory Compliance Agent — operating in coordinated autonomy to detect, respond to, and remediate threats with minimal human latency. Comparative evaluation against five baseline frameworks RBI's current mandated controls, NPCI's fraud detection stack, ML-only pipelines, SIEM-based monitoring, and Zero-Trust Architecture — demonstrates that Agent Shield achieves a fraud detection accuracy of 98.7%, reduces mean time to detection (MTTD) by 94%, and reduces false positive rates by 73% relative to existing approaches. The paper further analyses the macroeconomic implications of financial cybercrime on India's USD 3.7 trillion economy and presents a roadmap for regulatory adoption of agentic AI under the RBI Digital Payments Security Controls Directive