Enterprise AI should govern each model’s capabilities separately, not as one generic permission
rseroter · x · 2026-07-24
The post argues that you should not grant broad freedom to use more models until you can govern each model’s capabilities separately.
The attached diagram shows a Google Cloud environment where an application or agent can connect to either a Gemini base model or a Claude base model, but each model sits behind its own trust boundary and requires a deliberate grant.
The key point is that capability management should be separated by model and by data path. In the example shown:
- Gemini is treated as a separate trust boundary
- Claude is treated as a separate trust boundary
- grounding with Google Search has different storage and control characteristics than third-party web search
- Web Fetch and MCP connectors are called out as not covered in the same way
The post is essentially a governance warning for enterprise AI teams: don’t treat model access as one generic permission if the downstream data, storage, and connector behavior differ.
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