Advancing Privacy and Security in Generative AI-Driven Rag Architectures: A Next-Generation Framework

This paper presents an enhanced framework to strengthening privacy and security in Retrieval-Augmented Generation (RAG)-based AI applications. With AI systems increasingly leveraging external knowledge sources, they become vulnerable to data privacy risks, adversarial manipulations, and evolving regulatory frameworks. This research introduces cutting-edge security techniques such as privacy-aware retrieval mechanisms, decentralized access controls, and real-time model auditing to mitigate these challenges. We propose an adaptive security framework that dynamically adjusts protections based on contextual risk assessments while ensuring compliance with GDPR, HIPAA, and emerging AI regulations. Our results suggest that combining privacy-preserving AI with governance automation significantly strengthens AI security without performance trade-offs.