Can incumbent AI governance vendors close the gap?

Enterprise AI governance requires a control layer that can connect user identity and permissions with every model call, tool call, real-time policy decision, and correlated audit record across an organization’s AI environment. Hyperscalers such as AWS, Microsoft, and Google offer governance features within their own cloud platforms but lose visibility when organizations use multiple model providers or external tools. Enterprise platforms and GRC vendors, including ServiceNow and Salesforce, can define policies and manage risk but generally operate above the traffic layer, making real-time enforcement difficult without building new infrastructure. LLM gateways and MCP security tools can enforce controls within their specialized layers but often lack broader identity visibility and cross-layer correlation. The analysis argues that these limitations are structural because incumbent vendors each control only part of the AI stack, while comprehensive governance requires an AI control plane positioned across the connections between those layers.

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