Designing Evaluation Frameworks For Enterprise LLM Deployments In Regulated Environments

Abstract

Enterprise organizations are increasingly adopting large language models (LLMs) to support automation, decision-making, catering to customers, and managing knowledge. But using the technology in regulated areas like finance, health, insurance, and government raises significant concerns around compliance, privacy, equity, security, and accountability. Current evaluation approaches mostly emphasize technical aspects such as accuracy, fluency, and reasoning without mentioning enterprise readiness in the regulated environment. In this paper, we propose a framework to guide the evaluation of enterprise Large Language Models in regulated settings. The framework takes into account seven factors: accuracy, regulatory compliance, cybersecurity and privacy, bias, explainability and auditability, and enterprise readiness. The research suggests a weighted scorecard to inform the model of deployment, risk classification, and post-deployment monitoring. The study also investigates the contributions of governance methods such as human oversight, transparency, red-teaming, and post-deployment monitoring to enable safety and reliability. This approach bridges the gap between high-tech and enterprise risk management and offers enterprises a way to deploy scalable, safe Large Language Models that can be ready for enterprise. This study creates a governance approach to decision-making around safe, reliable, and scalable deployment of AI in critical domains.