Machine Learning-Driven Convergence Analysis in Multijurisdictional Compliance Using BERT and K-Means Clustering

This paper explores the application of machine learning techniques, particularly natural language processing (NLP), to compare and analyze two major data privacy frameworks. Our research examines the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Our research presents a new adaptive compliance system that uses BERT and clustering methods to show where GDPR and CCPA rules match and differ.Key contributions of this research include:A comparative analysis of GDPR and CCPA provisions, highlighting overlapping requirements such as data access rights and breach notifications and divergent aspects like the GDPR’s “right to be forgotten” versus the CCPA’s “opt-out of sale.”Our approach uses NLP methods to prepare complex legal documents by breaking text into tokens, normalizing word forms, and detecting important entities.Our machine-learning solution helps companies comply with multiple privacy laws by giving them clear directions.The study examines the difficulty of interpreting complex legal terms and suggests solutions through expert input and transparent AI systems.Our research connects legal knowledge with technical methods to help organizations create better data privacy compliance systems.