Prompt Security and Insider-Risk Defense in Banking AI Systems

Abstract: Artificial Intelligence (AI) assistants are being utilized in several banking systems to improve their efficiency, customer service capability, fraud detection and prevention capability, compliance monitoring system, and decision- making process. However, with the increasing usage of LLMs and intelligent assistants comes the emergence of severe cybersecurity issues including the prompt injection attacks, insider threat issues, access issues, and sensitive data exposure. Prompt injection refers to taking control of the AI system by injecting prompts and hidden instructions that enable security threats to bypass existing measures and reveal sensitive financial information or make the AI system do what it is not supposed to be doing. In the case of banks where AI systems are linked to customer records, transactions, and authorization processes, prompt injection and insider threat issues will lead to financial fraud and compliance problems. This paper highlights and mitigates the two most serious types of security risk associated with prompts and insider issues to the bank AI systems by developing a layered security strategy for prompt isolation and insider threat mitigation through role-based access control, human validation criteria, and restricted scope of impact zones over the duration of alert monitoring period. Simulation studies prove that our methodology can reduce the success rate of attacks and provide financial institutions with safe AI governance.

Keywords: Prompt Injection, Prompt Security, AI Banking Solutions, Internal Threats Detection, Information Protection from Leakage, LLM, Banking AI Assistants, Financial Cybersecurity, RBAC, Human-in-the-Loop Security, AI Governance, AI-based Banking Automation, Compliance Monitoring, Prevention of Fraud, LLM Security