Configure delegated access for GCP Vertex AI
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For Vertex AI, delegated access is typically implemented with a Google service account and short-lived OAuth access tokens generated through secure identity flows.
In production, prefer Workload Identity Federation or managed identity paths over long-lived JSON key files.
Production systems where static service-account keys are discouraged.
Centralized IAM/security operations with scoped permissions.
Environments requiring frequent credential rotation and strong audit trails.
GCP project with Vertex AI enabled.
Permissions to create/manage service accounts and IAM roles.
A service account dedicated to Flashgate AI provider access.
Flashgate AI LLM setup path: Configure an AI LLM.
Implement a secure token refresh mechanism (for example in a trusted backend service) that obtains fresh OAuth access tokens before expiry.
If your architecture needs a stable upstream interface, expose a small proxy that handles token acquisition and forwards requests to Vertex AI.
Use Configure an AI LLM with existing fields:
Choose the appropriate AI LLM Type for your Vertex integration.
Set API Endpoint to Vertex AI endpoint (or your proxy endpoint).
Set API Secret to the token/credential expected by that endpoint.
Set API Key only if your integration endpoint requires an additional key.
If your provider flow cannot be mapped directly to endpoint + secret/token, place the identity logic in a controlled proxy and configure Flashgate to call that proxy.
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