From Complexity to Clarity: AI / NLP ’s Role in Regulatory Compliance (2025)

Abstract: 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.

## introduction regulatory data compliance -ensuring an organization's data handling processes, practices, and documentation adhere to applicable laws and regulations -has become increasingly critical in today's business landscape. organizations must maintain comprehensive documentation, implement security measures, and regularly audit their practices to comply with regulations like the general data protection regulation (gdpr) @xcite in the european union, the california consumer privacy act (ccpa) @xcite in the u.s. state of california, among others. 1 the stakes are high, as non-compliance can result in substantial fines, legal consequences, and reputational damage @xcite . 1 while organizations must be compliant with several kinds regulations, such as data, financial, environmental, we focus on data-protection regulations due to (1) the significant body of nlp work in this field; and (2) the sizeable impact of these regulations on organizations' everyday functioning and expenses @xcite . the challenge of maintaining compliance has grown significantly. organizations often need to comply with multiple regulatory frameworks across jurisdictions. the traditional approach of manual compliance checking faces significant limitations in scale, consistency, and adaptability. as regulations grow more complex and organizations handle increasing volumes of data, manual verification becomes impractical and error-prone. recent advances in natural language processing (nlp) and large language models (llms) have demonstrated remarkable success in complex text analysis tasks. these models have achieved near human-level performance in document summarization @xcite , legal document analysis @xcite , and code understanding @xcite . their ability to process complex text at scale offers promising solutions for automating compliance processes. nlp and llms can assist with data compliance in several ways: analyzing privacy policies and compliance documents for completeness, verifying software systems against stated policies, making regulations accessible through question-answering systems, and helping prepare for regulatory changes. these technologies can reduce manual effort while improving accuracy and comprehensiveness, let alone facilitating accountability and governance, basic tenets of responsible ai. this survey examines the use of nlp and machine learning techniques for regulatory data compliance. while automated compliance has received significant attention in policy research and industry practice, the application of nlp techniques to this domain remains relatively underexplored in academic literature. despite promising advances, the field lacks standardized benchmarks and systematic comparisons of approaches, making it difficult to track progress or establish best practices. our survey provides the first comprehensive overview of nlp applications in automated data compli-ance, highlighting its current state and identifying promising future directions. this paper is organized as follows. section 2 establishes the case for automated data compliance by examining limitations of manual approaches. section 3 explores key problems and surveys current techniques. section 4 reviews available datasets and their characteristics. section 5 discusses the current state of the field and future directions. ## datasets the availability of high-quality datasets is crucial for advancing automation of compliance processes. privacy policy understanding datasets like the opp-115 corpus @xcite , mapp corpus @xcite , and maps policies dataset @xcite provide annotated privacy policies at varying scales, useful for developing tools that analyze privacy policies, summarize key practices, and improve user understanding.