Paper Title
VERITAS: An AI-Driven Compliance Analysis System Using LLM-Based Clause-Level Document Validation
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Registration ID: IJNRD_324281
Published ID: IJNRD2605139
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Keywords
compliance automation, large language models (LLMs), regulatory compliance, GDPR, semantic chunking, clause-level validation, Agentic Workflows, HIPAA
Abstract
As organizations need to comply with multiple compliance frameworks (such as GDPR and HIPAA), regulatory compliance has become increasingly complex. Unfortunately, validating documents manually against compliance standards is a time-consuming, error-prone process that is not scalable. This paper describes VERITAS, an AI-enabled platform that automates document validation through the use of agentic workflows. VERITAS leverages LLMs (Large Language Models) to conduct contextually aware analysis of unstructured documents and performs clause-level compliance evaluation based on predefined regulatory standards/rules. The platform uses a structured pipeline comprised of document ingestion, text extraction, semantic chunking, and parallel AI processing, enabling it to be scalable and efficient. In contrast to traditional keyword-based validating systems, VERITAS utilizes reasoning-based validation techniques to produce accurate and explainable results. The outputs generated by VERITAS include structured compliance scores, categorized results (pass, fail, warning), severity ratings, and evidence-based explanations. The platform also offers automated reporting capabilities that increase the readability and usability of audit documentation. Security features such as authentication and control of data access will ensure that sensitive data is handled securely. By reducing manual effort and increasing consistency, VERITAS enables compliance validation to be performed efficiently, transparently, and seamlessly using AI-driven methodologies.
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How To Cite (APA)
Dheeraj Patil, Ganesh Ghadge, Karan Dhokale, & Sahil Bagul (May-2026). VERITAS: An AI-Driven Compliance Analysis System Using LLM-Based Clause-Level Document Validation. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), b293-b300. https://ijnrd.org/papers/IJNRD2605139.pdf
Issue
Volume 11 Issue 5, May-2026
Pages : b293-b300
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Paper Reg. ID: IJNRD_324281
Published Paper Id: IJNRD2605139
Research Area: Other area not in list
Author Type: Indian Author
Country: Pune, Maharashtra , India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2605139.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2605139
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