From Complexity to Clarity: AI/NLP's Role in Regulatory Compliance

Regulatory data compliance is a cornerstone of trust and accountability in critical sectors like finance, healthcare, and technology, yet its complexity poses significant challenges for organizations worldwide. Recent advances in natural language processing, particularly large language models, have demonstrated remarkable capabilities in text analysis and reasoning, offering promising solutions for automating compliance processes. This survey examines the current state of automated data compliance, analyzing key challenges and approaches across problem areas. We identify critical limitations in current datasets and techniques, including issues of adaptability, completeness, and trust. Looking ahead, we propose research directions to address these challenges, emphasizing standardized evaluation frameworks and balanced human-AI collaboration. The Case for Automated Regulatory Compliance Modern organizations face increasingly complex regulatory requirements that govern how they handle data, develop and deploy software, and conduct business. Manual compliance checking -the traditional approach -faces several critical limitations that make it inadequate for today's needs. First, regulatory frameworks have grown significantly in complexity and scope. 2 For example, the GDPR contains 99 articles with intricate requirements, and organizations often need to comply with multiple such frameworks simultaneously. This complexity makes manual interpretation timeintensive and requires scarce, expensive expertise. 3 Second, compliance checking involves analyzing large volumes of documents and software systems. Organizations maintain numerous documents and software codebases that must align with regulatory requirements. Manual verification of all these artifacts is practically infeasible. Third, manual compliance checking is prone to human error and inconsistency. This risk increases when dealing with multiple jurisdictions or when regulations are updated. Recent advances in NLP/LLMs offer promising solutions to these challenges. LLMs can process and understand complex text, while specialized tools can automate document analysis and code checking. This paper surveys these automated approaches to regulatory data compliance, examining their current capabilities and limitations.