Responsible LLM Systems: Governance, Safety, and Evaluation Frameworks for Enterprise AI Agents

Abstract

Enterprise adoption of Large Language Models presents governance challenges due to their probabilistic nature and potential for generating harmful, biased, or non-compliant outputs. This article proposes a three-layer governance architecture for mission-critical enterprise environments across finance, healthcare, and legal sectors. The framework integrates pre-mitigation policy binding mechanisms, runtime safety enforcement systems, and post-mitigation audit capabilities. Novel contributions include enterprise-specific threat modeling, measurable evaluation protocols with defined thresholds, and compliance-aligned governance artifacts. The proposed architecture addresses gaps in existing governance approaches through systematic threat-to-control mapping, quantitative safety assessment protocols, and comprehensive audit trail specifications. Industry-specific implementation guidance demonstrates framework applicability while maintaining operational efficiency. Technical limitations including safety-utility trade-offs and adversarial adaptation challenges receive detailed treatment alongside mitigation strategies.