IRO Journals
Journal of Information Technology and Digital World

RAG-Based AI Compliance Monitoring and Report Generation System

Open Access
Published: 28 July, 2026
Pages: 195-209
  • Sivasakthi N. , Sivasakthi N.
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Sathyapriya P. , Sathyapriya P.
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Vishnu Priya R M. , Vishnu Priya R M.
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Paranthaman A.G. Paranthaman A.G.
    Assistant Professor, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Assistant Professor, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
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How to Cite

N., Sivasakthi, Sathyapriya P., Vishnu Priya R M., and Paranthaman A.G. 2026. “RAG-Based AI Compliance Monitoring and Report Generation System”. Journal of Information Technology and Digital World 8 (3): 195-209. https://doi.org/10.36548/jitdw.2026.3.005.
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Keywords

Enhanced Retrieval-Augmented Generation (ERAG)
Regulatory Compliance Monitoring
Legal Language Models
Semantic Retrieval
ChromaDB
Automated Compliance Reporting

Abstract

Compliance with regulatory rules becomes an increasingly complex task because of the constantly changing legal regulations and policy regulations. Currently, the existing approach to compliance verification is mostly based on the manual analysis of documents and is characterized by high time consumption, inconsistency, and high levels of operational risks. To solve the problem, this paper introduces CompVault, an Enhanced Retrieval-Augmented Generation (ERAG)-based Artificial Intelligence Compliance Monitoring and Report Generation System for intelligent regulatory compliance assessment. The introduced approach combines the semantic document retrieval, vector-based knowledge representation with ChromaDB, and contextual reasoning by means of the large language model to check compliance of organizational policies with regulatory requirements. It also determines the compliance gaps and compliance risk level and generates structured reports with recommendations to decision-makers. Web-based implementation was implemented using FastAPI, all-MiniLM-L6-v2 embeddings, and LexGLUE legal benchmark as a knowledge source about regulations. As a result, the following metrics were achieved: 97.42% accuracy, 96.88% precision, 97.15% recall, 97.01% F1-score, and 98.80% AUC-ROC. Therefore, the achieved results indicate that the ERAG-based framework can be used as an efficient, scalable, and explainable solution for regulatory compliance monitoring and automated report generation.

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