The Insider Risk of Artificial Intelligence in Financial Systems through the Lens of Large Language Models

<p><strong>Abstract</strong></p> <p>The integration of Large Language Models (LLMs) into financial systems introduces transformative opportunities alongside unprecedented risks particularly those related to insider threats. This paper examines the multifaceted security, privacy, and governance challenges of deploying LLMs in sensitive financial workflows, including accounts payable, forecasting, and client advisory. We present a taxonomy of emerging vulnerabilities such as prompt injection, data leakage, and cross-user exposure and analyze how these risks align with traditional and synthetic insider threat patterns. Drawing on real-world financial use cases, we introduce a structured risk matrix and propose mitigation strategies spanning technical controls (e.g., prompt isolation, logging) to high-level governance frameworks. The study concludes with practical recommendations for enterprise deployment and identifies key areas for further research, including model interpretability, ethical AI adoption, and proactive risk modeling.</p> <p><strong><em>Keywords: Large Language Models (LLMs); Insider Threats; Financial Systems Security; AI Governance; Prompt Injection; Data Leakage; Role-Based Access Control; Model Auditing; Explainable AI (XAI); Risk Assessment Matrix</em></strong></p>