Securing U.S. Critical Infrastructure with Autonomous Language Agents: A Trustworthy, Policy-Aligned Framework for High-Risk Enterprise Reasoning

Abstract

The rapid deployment of autonomous language agents across enterprise systems presents unprecedented security challenges for U.S. critical infrastructure sectors including energy, water, transportation, healthcare, and financial systems. This paper addresses the fundamental tension between the operational benefits of AI-driven decision-making and the security risks inherent in deploying autonomous reasoning systems within high-stakes environments. Through comprehensive analysis of existing literature, threat modeling, and policy frameworks, we identify critical gaps in current approaches to securing AI agents in critical infrastructure contexts. We propose a novel trustworthy AI framework comprising five integrated layers: Policy Alignment, Reasoning Verification, Human Oversight, Secure Execution, and Audit Governance. Our methodology synthesizes insights from NIST AI Risk Management Framework, cybersecurity best practices, and enterprise governance requirements to create a policy-aligned architecture specifically designed for high-risk reasoning scenarios. The framework addresses key threats including prompt injection, model hallucinations, decision manipulation, adversarial reasoning, insider misuse, and system drift through a comprehensive defense-in-depth approach. Evaluation demonstrates that the proposed architecture significantly enhances trustworthiness metrics compared to existing solutions while maintaining operational efficiency. This research contributes to the emerging field of AI governance by providing a practical, implementable framework that bridges the gap between theoretical AI safety principles and operational critical infrastructure protection requirements.