Large language models (LLMs) have revolutionized the field of regulatory audits, providing innovative solutions for complex legal compliance scenarios. In this paper, we introduce Simple Legal Compliance, a framework designed to automate regulatory audits by employing explainable LLMs. This approach facilitates the systematic inspection of legal documents and compliance requirements, streamlining the traditionally labor-intensive auditing process. The advanced capabilities of LLMs enable the system to comprehend intricate regulations and articulate clear insights regarding compliance status. A key component of this framework is the explainability layer, which ensures transparency in the model’s reasoning, allowing legal practitioners to trace each decision back to specific regulatory clauses and model predictions. Additionally, the feedback mechanism included within the system enhances its performance over time, learning from previous audits to improve accuracy and reliability. Experimental results across a range of regulatory domains reveal substantial reductions in both time and resource allocation compared to conventional audit practices. The results showcase the potential of this framework to enhance legal compliance workflows, providing legal professionals with actionable insights that foster more informed decision-making in complex regulatory environments.