Enterprise-Scale Risk Decision Intelligence Systems Using Large Language Models (LLMs) For Automated Governance, Compliance, and Policy Enforcement

Abstract

This study investigated enterprise-scale risk decision intelligence systems using Large Language Models (LLMs) for automated governance, compliance, and policy enforcement within digitally integrated enterprise environments. The research examined the influence of LLM capability integration, governance automation intensity, compliance intelligence capability, explainable artificial intelligence implementation, cybersecurity readiness, and data governance maturity on governance performance, compliance accuracy, operational resilience, organizational transparency, decision-making efficiency, and institutional accountability. A quantitative cross-sectional research design was adopted, and data were collected from 400 governance professionals, compliance officers, cybersecurity analysts, enterprise risk managers, and operational executives employed in medium-sized and large enterprises across finance, healthcare, information technology, telecommunications, logistics, manufacturing, and insurance sectors. Statistical analysis was conducted using SPSS and SmartPLS software packages through descriptive statistics, reliability testing, correlation analysis, multiple regression analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. The findings demonstrated that governance performance achieved the highest mean score (M = 4.31, SD = 0.63), followed by compliance accuracy (M = 4.27, SD = 0.66) and governance automation intensity (M = 4.26, SD = 0.65), indicating strong organizational adoption of AI-enabled governance infrastructures. Regression and Structural Equation Modeling findings revealed statistically significant positive relationships between governance variables and organizational performance outcomes. Cybersecurity readiness demonstrated the strongest predictive influence on operational resilience (β = 0.44, p < 0.001), while LLM capability integration significantly influenced governance performance (β = 0.41, p < 0.001). Compliance intelligence capability also positively predicted decision-making efficiency (β = 0.40, p < 0.001), whereas explainable AI implementation significantly strengthened organizational transparency (β = 0.35, p < 0.01). Reliability analysis further confirmed strong internal consistency across all constructs, with Cronbach’s alpha values ranging from 0.87 to 0.93. The findings demonstrated that enterprise-scale LLM governance systems significantly improved governance coordination, compliance reliability, institutional accountability, operational resilience, and predictive governance intelligence across digitally interconnected enterprise ecosystems.