Securing RAG Pipelines: Access Control and Permissions

As organizations deploy Retrieval-Augmented Generation (RAG) systems to query internal knowledge bases, ensuring that users only access information they are authorized to see is critical. Standard AI pipelines often lack built-in security, creating major compliance and privacy risks. This text-only course guides you through the foundational concepts and practical strategies needed to secure your RAG applications. You will transition from basic pipeline setups to robust, secure architectures that respect user permissions and maintain strict data privacy. What you'll learn: - Understand foundational security concepts in RAG, including data leakage risks and the principle of least privilege. - Implement document-level security using metadata filtering within vector databases. - Configure Role-Based Access Control (RBAC) to restrict document retrieval based on user identity. - Apply modern zero-trust security principles to LLM orchestration workflows. - Design audit logging mechanisms to track data retrieval paths and monitor compliance. - Address security vulnerabilities unique to vector search and generative AI architectures. The course begins with core definitions of access control in AI, moving systematically through user authentication, vector database filtering techniques, and secure pipeline integration. You will study practical, text-based architectural patterns and code snippets that you can apply directly to your own projects. Designed for software developers, data engineers, and security beginners, this course requires no advanced prior experience in machine learning. Start reading today to build secure, compliant, and trustworthy AI retrieval systems.