Trusted Multi-Agent Generative AI for Real-Time Defense of U.S. Digital Payments: A Privacy-Preserving Governance Framework for Critical and Emerging Financial Technologies

Abstract

The proliferation of autonomous, LLM-based multi-agent systems in financial services introduces critical challenges in verifying authenticity, intent, and overall system integrity, particularly within decentralized, trustless environments where emergent behaviors and opaque decision-making undermine regulatory compliance and operational safety. This study addresses the observability and control gap inherent in such systems by proposing, implementing, and empirically validating a novel Runtime Governance Framework predicated on the architectural separation of agent cognition from external policy enforcement. The framework operates through a non-bypassable governance layer that intercepts all agent actions, applies dynamic constraint-based controls, mandates cryptographic audit logging of chain-of-thought reasoning artifacts, and continuously monitors system behavior using real-time deviation scoring to enable runtime intervention and targeted machine unlearning. Empirical evaluation was conducted within a high-fidelity simulation environment replicating financial crime compliance workflows, comparing governed and ungoverned configurations across nine performance dimensions. Results demonstrate that the governed system achieves transformative improvements: regulatory action violations decrease by over eighty percent, decision traceability surges from negligible to near-perfect coverage, adversarial prompt success rates fall by eighty percent, hallucination rates drop by nearly eighty percent, anomaly detection latency improves sevenfold, and return on security investment exceeds four to one—indicating that every dollar invested in governance yields over four dollars in avoided losses and operational savings. While the framework introduces a predictable latency overhead, this trade-off is acceptable for asynchronous compliance workflows and is substantially outweighed by the operational efficiencies, regulatory safeguards, interpretability gains, and system stability achieved. This research provides a validated architectural blueprint for the secure, trustworthy, and compliant deployment of autonomous agentic AI in regulated financial environments, establishing that effective governance must be external, enforceable, and runtime-adaptive rather than static and pre-deployment focused.