Multi-Cloud RAG Governance: Unified Policy Enforcement Across AWS, Azure, and Google Cloud

Most enterprise AI teams do not live in a single cloud. A team might use Azure OpenAI for inference, store embeddings in Google Vertex AI Search, run a legacy pipeline on AWS OpenSearch, and manage identities across all three. The retrieval layer has to work across all of them — and so does the governance layer.

With the addition of Vertex AI Search (and Vertex AI Vector Search, shipping alongside it), Gateco now provides native connectors for all three major clouds: AWS OpenSearch, Azure AI Search, and both Vertex AI products on Google Cloud. Every one of these connectors uses the same policy engine, the same identity resolution pipeline, and the same audit trail.

The Multi-Cloud Retrieval Problem

When your vectors span clouds, the access control problem multiplies. Each cloud platform has its own IAM model, its own notion of a document or index, and its own approach to search security. Building per-cloud policy logic means you end up with three different enforcement mechanisms, three different audit formats, and three different places to configure who can see what.

The typical workaround is to centralize retrieval through a single intermediary service that handles all the cloud-specific logic. That works until the intermediary becomes a bottleneck, or until you need the audit trail to reflect the actual retrieval path, not a wrapper around it.

One Policy Engine, Multiple Clouds

Gateco's connector model separates retrieval mechanics from policy enforcement. When you connect an Azure AI Search index, a Vertex AI data store, or an OpenSearch cluster, you are telling Gateco where to retrieve vectors. The policy engine sits above the connector — it does not care which cloud the vectors came from. The same RBAC rule that says "only members of the engineering group can access resources classified internal" applies identically to a Pinecone namespace, an Azure AI Search index, and a Vertex AI data store.

This matters most in mixed-cloud pipelines: a query arrives from an AI agent, Gateco resolves the principal, evaluates all matching policies, and dispatches to whichever connector holds the relevant embeddings. The agent gets back a filtered result set and a unified audit event. The cloud boundary is invisible to the policy layer.

Connector Parity Across the Three Clouds

The three enterprise search connectors in Gateco's Tier 2 catalog now cover the major managed search platforms. AWS OpenSearch supports ranked keyword, vector, and hybrid search with Gateco's RRF fusion layer. Azure AI Search has native hybrid with its own RRF implementation. Vertex AI Search has native hybrid via Google's Discovery Engine ranking model. All three support retroactive registration — you do not need to re-index to get policy coverage on existing data.

The key difference is alpha: Azure AI Search and Vertex AI Search both use their own internal relevance models, so Gateco's alpha parameter is advisory (it triggers a warning if you set a non-default value). OpenSearch uses Gateco's boost-based emulation. For teams migrating between clouds, the behavior is intentionally consistent — the policy layer is identical, even if the underlying ranking math differs slightly.

Identity Across Clouds

Retrieval governance without identity context is just keyword filtering. Gateco's four IDP adapters — Azure Entra ID, AWS IAM Identity Center, GCP Cloud Identity, and Okta — sync your organization's principals and group memberships into the policy engine. An engineer who belongs to the platform-team group in Azure AD will be treated as a platform-team member regardless of which cloud connector serves the retrieval request. Cross-cloud policy conditions work because identity is resolved once, before the connector is invoked.

Getting Started with Multi-Cloud Retrieval

If your organization spans multiple clouds, connecting each retrieval backend to Gateco is additive — you do not need to change your existing pipelines or re-index any data. Connect your first cloud connector from the Gateco dashboard, write a policy, and run a dry-run simulation against your team's principals using the Access Simulator before enabling live enforcement. Each new connector you add shares the same policies and identity graph.