# Central Identity Gateway for Federated Kubernetes and AI Platforms A unified approach to user identity across distributed AI platforms reduces login prompts by 55%. - **Source:** [Nvidia Developer Blog](https://developer.nvidia.com/blog) - **Published:** 2026-09-03 - **Topic:** [Software & Engineering](https://brifing.si/c/engineering) - **Original article:** - **On Brifing:** https://brifing.si/s/8aa775a3-1fe2-45b5-bedf-fa7d95bda0aa ## The Challenge of Federated Identity Modern AI platforms span multiple clusters and cloud environments, requiring users to authenticate repeatedly as they move between tools. This creates friction and security risks. A unified identity solution is needed to maintain user context across distributed execution environments without compromising security or forcing each cluster to reimplement identity logic. ## NVIDIA's Solution NVIDIA implemented a central identity gateway pattern that reduced repeated login events by 55% across their internal developer platforms. The solution uses standard OpenID Connect (OIDC) with a shared session store, allowing regional gateways to validate user identity without managing independent OIDC flows. This approach coordinates token refresh and logout platform-wide while reducing upstream identity-provider load. ## Technical Architecture The architecture consists of a central gateway that owns the platform session and data-plane gateways that validate sessions through a shared API. It uses a minimal /gateway/userinfo validation endpoint, stateless regional gateways, and a shared Redis-backed session store with explicit TTLs. Security measures include mutual TLS between gateways and stripping inbound identity headers before injecting trusted ones. ## Benefits for Users Users experience a unified platform across notebooks, catalogs, query tools, dashboards, and AI assistants without repeated login prompts. The solution enables consistent logout behavior and allows AI assistants to act with delegated user identity across different data planes, creating a seamless workflow experience. ## Benefits for Platform Teams Platform teams gain a reusable foundation for identity management that scales with active users rather than user-tool combinations. The architecture standardizes identity headers for downstream services and reduces the load on upstream identity providers. It also provides clear failure behavior for session-store unavailability. ## Implementation Details The implementation uses OAuth2 Proxy for local environment experimentation with OIDC login, cookie handling, and Redis-backed sessions. It follows the Istio external authorization sample to define the Auth Gateway interface and adds Rego policy evaluation with the OPA Envoy Istio example. Authorino provides an integrated reference for various validation scenarios. ## Problem Without Solution Without a shared identity propagation model, each service gateway performs a full OIDC redirect independently. This results in multiple logins, isolated sessions, and no shared state. The distributed session ownership model creates structural problems as platforms expand, leading to inconsistent behavior and maintenance challenges. ## Distributed vs Centralized The distributed session ownership model keeps each cluster independent but doesn't allow identity state to move cleanly across platforms. The centralized approach with a dedicated identity gateway owns login, session, and logout behavior while allowing enforcement components to focus on access control rather than identity state management. ## Security Considerations The architecture includes several security guardrails: mutual TLS or workload identity between gateways, stripping of inbound identity headers before injecting trusted ones, and explicit failure behavior definitions for session-store unavailability. These measures ensure secure identity propagation without exposing raw tokens to every application. ## Applicability While implementation details vary by organization, the core design applies to platform teams running federated Kubernetes environments, multi-cloud data platforms, machine learning workbenches, internal developer portals, or AI application stacks with multiple authenticated tools. It's particularly relevant for AI and data platforms where data and compute stay close to their sources. ## User Workflow Example A typical workflow might start in a central portal, then move to a governed dataset, launch a notebook where that data resides, and invoke an assistant calling services in another cluster. The identity gateway ensures this workflow feels unified despite crossing multiple control-plane and data-plane boundaries where conventional SSO would fail. ## Future Implications This approach sets a foundation for more sophisticated identity management in distributed AI platforms. As platforms grow more complex with additional tools and services, having a robust identity propagation mechanism will become increasingly critical for both user experience and security compliance across enterprise AI ecosystems. ## Sources covering this story - https://developer.nvidia.com/blog/how-to-carry-user-identity-across-federated-kubernetes-and-ai-platforms/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._