Developing an AI-Powered NLP Model for Automated Cross-Border Legal Interpretation in Cybercrime Jurisdiction

The increasing complexity of cybercrime jurisdiction across multiple legal frameworks necessitates an AI-powered NLP model for automated legal interpretation. Disparities in national laws, language barriers, and evolving cyber threats create challenges in cross-border cybercrime investigations, leading to delays and inefficiencies in legal cooperation. To address these issues, this study proposes the Cross-Border Cybercrime Investigations using AI (CBCI-AI) framework, which leverages Natural Language Processing (NLP), machine learning, and legal ontologies to analyze, interpret, and harmonize legal texts from different jurisdictions. This AI-driven approach ensures accurate translation, contextual interpretation, and automated compliance checks, facilitating real-time legal understanding across multiple legal systems. The CBCI-AI framework enhances international legal cooperation, reduces interpretation inconsistencies, and streamlines cross-border cybercrime investigations. It enables law enforcement agencies, policymakers, and legal professionals to swiftly navigate complex cybercrime cases while ensuring compliance with international treaties. Experimental findings indicate that the CBCI-AI framework significantly improves legal interpretation accuracy, reduces processing time, and enhances jurisdictional clarity. By automating legal text analysis, this method promotes efficiency in global cybercrime investigations and fosters improved collaboration between international law enforcement bodies.