Over the past three years, the AI landscape has evolved significantly, transitioning from basic language models to sophisticated AI agents capable of independent action, which has led to increased usage and highlighted security challenges. The introduction of Large Language Models (LLMs) and Generative AI in 2023 has exacerbated these issues, particularly around data retrieval and confidentiality. Traditional access controls like Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) fall short in providing the necessary security, prompting the adoption of Fine-Grained Authorization (FGA) systems like OpenFGA that use Relationship-Based Access Control (ReBAC) to enforce dynamic and contextual permissions. OpenFGA, inspired by Google's Zanzibar system, allows for flexible, real-time authorization decisions by defining relationships between users and resources. This approach is particularly crucial in Retrieval-Augmented Generation (RAG) AI systems, which require secure data handling without compromising speed or scalability. OpenFGA's integration with systems like Couchbase Vector Search enables secure AI applications by ensuring access to only authorized data, thus preventing sensitive information disclosure and supporting robust AI deployment in sectors like healthcare, finance, and legal services.