In our previous article, we introduced the Model Context Protocol (MCP)—the backbone that allows AI agents to take secure, purposeful actions. Today, we go a step further and show how to architect a complete Zero Trust AI workflow using SecureAuth’s Microperimeter™.
If your AI agent is calling APIs—whether for weather data, financial tools, or HR systems—there’s one non-negotiable requirement: identity-first access control, enforced at the exact moment of execution. That’s what this architecture delivers.
The Core Architecture: AI Agent + MCP + SecureAuth
Here’s the basic flow of a secure agentic AI system using SecureAuth:
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- User logs in via SecureAuth Identity Provider (IdP)
- AI agent requests an OAuth token for specific scopes (e.g., forecast, alerts)
- SecureAuth issues a token if the user satisfies policy (e.g., group membership)
- AI agent calls MCP Server, passing the token
- MCP Server calls SecureAuth Microperimeter™, asking:
- Is this user authorized to access this API?
- Does the token include required scopes and audience claims?
- SecureAuth approves or denies the request
- If allowed, MCP returns data to the agent, which includes it in its response
This pattern protects APIs from misuse—whether intentional or accidental—by enforcing strict, real-time authorization based on policy.
Zooming Into the Key Components
OAuth + OIDC
SecureAuth acts as the authorization server, issuing access tokens with scopes (e.g., read_forecast) only if the user meets defined criteria—such as being in a certain group or geographic location.
Microperimeter Authorizer
This component serves as the Policy Decision Point (PDP). It sits near your APIs or MCP Server and evaluates whether a request should proceed. It checks:
- The access token’s claims (e.g., scopes, audience, expiration)
- Contextual data (e.g., IP address, time of day)
- Dynamic policies defined in the SecureAuth dashboard
SPIFFE Audience Claims
Every token includes an aud (audience) claim. This tells the receiving API which services the token is meant for. SecureAuth issues tokens in SPIFFE-compatible format so that agents can’t misuse tokens intended for other services.
Dashboards and Auditing
Every decision made by the Microperimeter is logged in SecureAuth’s dashboard—who requested access, what scopes they had, what endpoint was called, and whether it was allowed. Security teams get full transparency.
Real Use Case: Forecast Access
Let’s say you have two users:
- testuser1 is part of the “Forecast Users” group
- testuser2 is not
Your weather API (invoked via MCP) has two endpoints: /get_forecast and /get_alerts.
✅ When testuser1 logs in:
- SecureAuth issues a token with forecast and alerts scopes
- Microperimeter allows both endpoints to be called
- The agent returns a full weather report
❌ When testuser2 logs in:
- SecureAuth issues a token without required scopes
- Microperimeter denies access
- The AI agent returns a generic response, with no live data
This is Zero Trust in action: never trust, always verify—at every request.
Developer-Friendly Architecture Secure AI WorkFlow
The best part? Developers don’t have to hardcode access checks or build a custom policy engine. SecureAuth handles:
- Token issuance and validation
- Scope-based access control
- Policy management via UI
- API integration with minimal code
Your team focuses on building AI agents. We secure every action they take.
Architecting Trust from the Ground Up
This is how you turn AI from a risk into a differentiator. SecureAuth’s Microperimeter makes it possible to enforce Zero Trust at API speed, prevent unauthorized access by default, and log and audit every decision without slowing innovation.
Next up: we’ll walk you through how to build your own secure AI agent, from login to live deployment.