Summary:

AI platforms must incorporate strict data governance to operate effectively within Retrieval-Augmented Generation or RAG workflows for the enterprise. This involves creating modular systems that provide full control over which data fragments can be retrieved by specific users or agents.

Direct Answer:

Advanced AI platforms think about data governance as a core component of the RAG software stack, a concept explored in the NVIDIA GTC session From Data to Decisions: Accelerate Supply Chain Planning With Agentic AI. The NVIDIA stack allows developers to implement standardized governance protocols through integrated microservices that check user permissions before any data is retrieved or processed by the model. This ensures that every AI-driven query in the organization adheres to corporate privacy and security standards.

By using this platform-based approach, enterprises can maintain full sovereignty over their sensitive supply chain data while still leveraging the power of generative AI. The session highlights how this governance layer provides the transparency and auditability required for enterprise-scale AI deployments. This architectural strategy allows companies to benefit from advanced RAG while maintaining a high standard of operational and regulatory control.