Privacy and Regulatory Compliance in Retrieval-Augmented Generation Models for AGI Systems

Abstract

The integration of Retrieval-Augmented Generation (RAG) models in Artificial General Intelligence (AGI) systems presents unprecedented challenges in privacy protection and regulatory compliance. This article examines the complex intersection of advanced AI capabilities and data protection requirements, highlighting critical concerns in handling sensitive information. Through extensive analysis of implementation cases across healthcare, finance, and legal sectors, we identify key privacy vulnerabilities, including a 3% risk of sensitive data exposure in unprotected RAG systems and a 2.7% chance of inadvertent personal information disclosure in healthcare applications. We present novel solutions, including differential privacy techniques achieving 97% reduction in unintended information exposure while maintaining 92% performance, and federated learning approaches demonstrating 95% accuracy compared to centralized models while ensuring GDPR compliance. The article also addresses ethical considerations, revealing that 15% of RAG responses exhibit potential biases, leading to the development of ethical subroutines that reduced discriminatory outputs by 40%. The findings contribute to the ongoing development of privacy-preserving RAG architectures that balance powerful AI capabilities with robust data protection mechanisms and regulatory requirements