How to Build a Secure AI Agent Using SecureAuth and MCP

In our previous article, we explored the architecture of a secure AI workflow using the Model Context Protocol (MCP) and SecureAuth’s Microperimeter™. Now, it’s time to build one.

This guide shows you how to implement a simple AI agent that enforces identity-based access before touching any data without making role assumptions or static checks while interacitng with an external API.

Our example: A weather assistant that only returns forecasts to users who have permission to see them.

What You’ll Need to Build a Secure AI Agent

• A SecureAuth trial tenant (sign up [here])
• Ollama (or any LLM you can invoke via API) to run a local model like llama3.2
• Python or Node.js for building the agent and MCP server
• A basic understanding of OAuth2 (scopes, access tokens)

This setup lets you build and test locally with no surprises.

Step 1: Configure Your SecureAuth Tenant

1. Create an OAuth Application

• Go to your SecureAuth dashboard
• Create a new app named “AI Agent”
• Note the client ID, client secret, and OIDC discovery URL
• Set redirect URI to: http://localhost:8000/callback

2. Create the Gateway (Microperimeter)

• Create a new Gateway called mcp-weather-service using the Standalone-Authorizer model
• Start the authorizer following the quickstart guide
• Prepare an json file to define API endpoints:

3. Define Scopes and Audience Claims

• Create scopes like forecast and alerts tied to the mcp-weather-service
• Configure audience claims for each scope

4. Assign Access Policies

• Example: only users in the forecast_users group get the forecast scope
• Policies are enforced at both token issuance and API access

Step 2: Build the MCP Server

Start with a basic MCP server (e.g., fork from Anthropic’s quickstart)

Modify the service to:

• Accept access tokens as a parameter
• Validate token by calling SecureAuth’s Microperimeter’s /request/validate endpoint
• If denied, return a blank or generic result

Step 3: Build the AI Agent

Your agent should do the following:

• Prompt user to log in via OIDC
• Request OAuth scopes: forecast, alerts
• Receive access token upon login
• Call the MCP server, passing the token
• Receive weather data (if allowed)
• Combine user prompt + weather data → send to LLM
• Return final AI-generated response based on real data and identity-aware access

You can spin up a simple front end interface using a tool like Flask, Fast API, or Express.

⚠️ Security Note: This example uses simplified flows for educational purposes. In production, always use secure token storage, PKCE, and HTTPS.

Test Case: Role-Based Access in Action

Let’s look at what happens when two different users log in.

User A (has access):

• Token includes forecast scope
• Agent retrieves weather data
• LLM provides detailed, context-aware answer

User B (no access):

• Token lacks required scope
• API call is blocked
• LLM returns a generic response

All interactions are logged and auditable via SecureAuth’s central dashboard.

Conclusion: From Prototype to Production

With this prototype, you’ve now built:

• A real AI agent secured by modern OAuth flows and identity policy
• Role-based access to critical APIs
• A working integration with SecureAuth’s Microperimeter Authorizer
• Full visibility into every interaction

The takeaway? AI agents can be secure by default—when identity leads and policy enforces it.

Next up: Why authorization (not authentication) is the real control plane for trust in agentic AI systems, and how SecureAuth empowers you to scale securely.