Security and Governance Model
AI Team governance separates workforce capability from authorization to use that capability.
Authorization model
A useful baseline is:
Human authorization ∩ Digital Employee authorization ∩ workflow policy = permitted access or action
A tool being technically available does not mean every Digital Employee or directing human may invoke it.
Control boundaries
| Boundary | Control |
|---|---|
| Human identity | Determines the directing user’s organizational entitlements |
| Digital Employee identity | Limits access and actions to the employee’s job |
| Workflow policy | Applies stage-specific restrictions and approval requirements |
| Customer context | Controls authoritative sources and information access |
| Tool/capability access | Limits systems and actions that can be invoked |
| Human gate | Reserves consequential decisions for organizational authority |
| Audit | Preserves required evidence, activity, and approvals |
Deployment boundary
Security controls can be implemented inside a customer-controlled cloud or private environment. An AWS-based deployment, for example, can keep customer data, identity, permissions, logs, connectors, and runtime services within the customer’s approved infrastructure boundary.
The security model is intentionally provider-independent so equivalent controls can be implemented in other approved environments.
Customer and Compound Leverage separation
Customer proprietary information and enterprise access remain on the customer-controlled side of the architecture. Compound Leverage-controlled services can expose protected capabilities without requiring proprietary implementation logic to be distributed into the customer environment.
See Customer AI Control Plane, AI Agent Governance and Controls, and MCP and AI Teams.