Operationalizing The NIST AI RMF For Smes — Top National Priority (AI Safety) And Perfect For Your Data/IT Toolkit; Produce A Lean Control Catalog, Audit Checklist, And Incident Drill For Real LLM Workflows

Abstract

The widespread integration of large language models (LLMs) into small and medium enterprises (SMEs) is operating at both transformative and heightened risk. Unlike big companies, SMEs have fewer resources, often with less robust governance in place to ensure safe and trustworthy AI deployment. The U.S. National Institute of Standards and Technology (NIST) published the Artificial Intelligence Risk Management Framework (AI RMF), a national standard to help guide responsible use of AI. However, realizing these principles in practical mechanisms applicable to SMEs is an outstanding challenge. This paper includes a proposal for lean operationalization, with a control catalog, audit checklist and incident drill designed for LLM workflows. Using a 3-phase mixed-method methodology - risk mapping, stakeholder workshops and pilot simulations - the study shows that SMEs can gain a measurable 16% reduction in operational risk exposure by embedding lightweight governance controls. This research provides a pragmatic contribution to AI safety by ensuring some national priorities are aligned with SME realities.