Artificial Intelligence-Driven Legal Governance: A Computational Framework for Regulatory Decision Support Using Large Language Model Prompt Engineering

Abstract

Recent advances in large language models (LLMs) and instantaneous response engineering have created new possibilities for strengthening legal governance and modernising regulatory oversight. Conventional legal systems increasingly operate under pressures arising from expanding volumes of information, divergent interpretations, and delays in responding to newly emerging societal risks. To address these challenges, this study proposes an integrated IT framework, termed the AI-assisted legal governance system (ALGS), which incorporates instantaneous response engineering from LLMs, natural language processing, and machine learning into regulatory decision-making. ALGS is organised around four interconnected components: semantic analysis of legal texts, AI-driven risk identification, intelligent compliance monitoring, and automated generation of regulatory advice. A hierarchical instantaneous response architecture is developed and evaluated to decompose complex legal reasoning into structured sub-tasks, enabling LLMs to undertake legal interpretation, case-based reasoning, and regulatory gap analysis with improved accuracy and auditability. Experimental assessment using publicly available legal materials, including EU General Data Protection Regulation compliance documents and US stock exchange records, demonstrates a 23.7% improvement in regulatory classification accuracy and a 41.2% reduction in human review time compared with basic methods. The study further considers ethical issues surrounding AI autonomy in legal decision-making, bias reduction, and the continued requirement for human supervision. The findings suggest that carefully structured rapid-response architectures can enhance regulatory and legal governance capacity while maintaining the constitutional principles of due process and the rule of law.