Adversarial Threats In AI-RPA Financial Systems: Security Challenges And Defense Strategies

Abstract

The convergence of Robotic Process Automation and Artificial Intelligence into financial services has redefined capabilities operations while at the same time bringing in sophisticated adversarial threat vectors that test conventional security models. Financial institutions today face advanced attack methods such as data poisoning attacks that taint AI model training processes, evasion attacks that tamper with inference-time inputs to evade detection systems, and interface weaknesses that compromise communication channels among AI decision-making systems and RPA execution engines. Modern adversarial methods exhibit outstanding efficacy, with data poisoning attacks attaining more than ninety percent success rates while sustaining stealth properties evading typical validation processes. Reinforcement learning-based evasion frameworks are capable of lowering fraud detection accuracy from baseline performance levels down to severely degraded states through calculated manipulation of transaction attributes. The automated nature of these combined systems escalates the consequences of attack exponentially, with a single compromised AI model able to initiate thousands of invalid RPA actions in an hour across key financial processes. Defense mechanisms must be governed with thorough frameworks involving Zero Trust architectures, adversarial training approaches, ensemble model methodologies, and real-time monitoring. Yet the changing threat landscape introduces recurring challenges as attackers use generative AI technologies and automated reconnaissance techniques to create more advanced attack vectors that will surpass traditional defensive capabilities.