Data-Centric Security for Generative AI Systems: Protecting Sensitive Data against Leakage, Inference Attacks, and Unauthorized Model Access

Abstract

The growing use of generative artificial intelligence in enterprise and public-sector environments has introduced significant concerns regarding the protection of sensitive data. Generative AI systems process large volumes of information across training, fine-tuning, retrieval, inference, and output stages, creating multiple opportunities for unauthorized disclosure and misuse. This study examines data-centric security as an approach for protecting sensitive information against data leakage, membership inference, model inversion, prompt-based attacks, retrieval-related exposure, model extraction, and unauthorized access to AI services. It reviews major attack surfaces across the generative AI lifecycle and evaluates security controls including data classification, minimization, encryption, privacy-preserving learning, identity and access management, secure retrieval-augmented generation, output filtering, data loss prevention, and continuous monitoring. Based on these findings, the study proposes a data-centric security framework that links data sensitivity, access policies, model controls, and governance requirements throughout the AI lifecycle. The framework emphasizes that security controls should remain tied to sensitive information regardless of where the data is stored, processed, retrieved, or generated. The study provides practical guidance for organizations seeking to reduce privacy and confidentiality risks while maintaining controlled and accountable use of generative AI systems.