In the dynamic landscape of global data privacy and regulatory compliance, the integration of Retrieval-Augmented Generation (RAG) with a custom BERT-GRC classifier represents a pivotal advancement in Governance, Risk, and Compliance (GRC) technologies. This method introduces a scalable framework that combines the contextual depth of Large Language Models (LLMs) with real-time data retrieval to deliver actionable insights for compliance management. The BERT-GRC classifier identifies regulatory gaps in organizational policies, aligning them with evolving standards such as NYCRR 500 and GDPR. By incorporating seq2seq transformers with a dense vector index, the proposed RAG system enhances the precision and adaptability of compliance assessments, surpassing traditional methods limited by static data and manual analysis.
This system has been validated through case studies, including automated gap analysis and policy alignment. Experimental results demonstrate significant improvements in compliance gap detection and response times, highlighting the system’s capability to dynamically adapt to new regulatory demands. The integration of these AI-driven solutions underscores the transformative potential of RAG with a fine-tuned BERT classifier in streamlining GRC processes and reinforcing cybersecurity defenses.
