Master Architecture
Layered authorization for AI execution chains.
AI IAM governs whether a user may delegate intent to an AI agent, whether that agent may use specific capabilities, whether the user is entitled to the underlying data, and whether the complete runtime action is permissible under purpose, context, and risk constraints.
Reference Document
AI Agent IAM Reference Architecture Diagram
Interactive diagram — all 8 layers, control plane, and enforcement pipeline.
Interactive AI IAM Architecture
Selected Architecture Element
Agent
Delegated Actor
The AI agent acting under delegated, scoped authority.
Enforces
- • Agent identity
- • Delegation scope
- • Allowed agent access
Audit Evidence
- • Agent ID
- • Delegation token
- • Agent owner
Runtime Decision Function
Decision = f(user, agent, capability, action, resource, purpose, context, risk)
Every request is evaluated dynamically across identity, capability, data entitlement, purpose, runtime context, and risk.
Advanced Control Elements
Selected Control Element
Delegation Model
The delegation model defines how a user grants bounded authority to an AI agent for a specific task. The agent does not inherit unlimited user access; it receives scoped execution authority constrained by user entitlements, agent capabilities, purpose, resource classification, expiration, and runtime context. Delegation should be task-bound, time-bound, revocable, and auditable. This prevents agents from becoming uncontrolled proxies for user access and creates a clear chain of accountability from the user to the agent to the action performed.
Key Principle
Guardrails guide model behavior, but enforcement must occur at the data, tool, and policy decision layers.