Sarcouncil Journal of Engineering and Computer Sciences

Abstract: This article presents a comprehensive framework for securing enterprise AI systems across their entire lifecycle, with a focus on Retrieval-Augmented Generation (RAG), fine-tuning, and data interaction interfaces. The architecture advocates for a "protection by design" philosophy that embeds robust authorization controls into every layer of the AI stack. Beginning with secure data preparation and storage, the framework addresses vector database vulnerabilities through the use of authorization metadata, section-level classification, and encrypted embeddings. It then explores access-controlled retrieval pipelines that maintain protection boundaries during inference through identity propagation, permission-based query augmentation, and multi-stage retrieval processes. For model adaptation, the article details strategies for safeguarding training data and hardening fine-tuning pipelines to prevent the extraction of confidential information. Finally, it examines protection mechanisms for user interaction interfaces, including natural language query fortification and comprehensive audit capabilities. Throughout, the article emphasizes that effective AI security requires not merely perimeter defenses but deeply integrated protection measures that preserve functionality while maintaining strict confidentiality boundaries.