# Secure Model Context Protocol with Cerbos authorization

Control what AI agents and tools can access with policy-driven authorization powered by Cerbos.

### Tool authorization

Control which MCP tools each user or role can invoke with fine-grained Cerbos policies

### Dynamic permissions

Enforce context-aware access control that adapts based on user identity, role, and request attributes

### Zero trust for AI agents

Apply least-privilege access to every tool call and resource request in your MCP server

## How Cerbos works with Model Context Protocol

AI agents and tools introduce a new class of authorization challenges. They act on behalf of users, access sensitive data, and chain operations, all of which need fine-grained access control.

Cerbos provides policy-driven authorization that controls what AI systems can do, which data they can access, and on whose behalf. Policies are written in human-readable YAML and evaluated at request time.

With Cerbos and Model Context Protocol, you get guardrails that scale with your AI adoption, centrally managed policies, full audit trails, and sub-millisecond decision times that don't slow down agent workflows.

### How Cerbos secures Model Context Protocol agents

1. **Define policies for AI agent actions and data access**: Write YAML policies that specify which tools, data, and operations each agent or user can access.
2. **Agent requests authorization from Cerbos**: Before performing an action, the AI agent sends the user context, tool, and target resource to the PDP.
3. **Cerbos evaluates context, tool, and resource**: The PDP applies fine-grained policies considering the user's identity, the requested tool, and the target data.
4. **Agent proceeds or is denied**: Cerbos returns an allow or deny decision and the agent framework enforces it, with a full audit trail.

### Security risks of unsecured AI agents

Without authorization at every tool call, AI agents introduce risks that traditional application security doesn't cover:

- **Privilege escalation through tools.** An agent with unrestricted tool access can perform actions the delegating user isn't authorized for — reading sensitive data, modifying resources, or invoking admin operations.
- **Incomplete identity context.** Agents often carry only a partial user reference. Without the full profile, policies can't enforce department-level, role-based, or attribute-based restrictions accurately.
- **No audit trail.** Without authorization checks at each step, there's no record of what the agent did, on whose behalf, or why it was allowed — making incident investigation and compliance reporting impossible.
- **Transitive trust.** When agents chain tool calls or delegate to other agents, permissions can expand silently if each step isn't individually authorized.

### Richer agent decisions with Cerbos Synapse

When an AI agent makes an authorization call, it often includes only an agent ID or a partial reference to the delegating user. [Cerbos Synapse](/content/product-cerbos-synapse/index.html) enriches agent requests with the full user profile from your identity provider, resource metadata from your data stores, and the agent's own constraints — so the PDP receives complete context for every decision without the agent needing to assemble it.

## FAQ

### How does Cerbos secure Model Context Protocol agents?

Cerbos evaluates fine-grained policies at every tool call and data access, ensuring AI agents only perform actions and access data they are authorized for, on behalf of the requesting user.

### Can I audit what my AI agents are doing?

Yes. Every authorization decision is logged with full context, the principal, resource, action, and result. This gives you a complete audit trail of AI agent behavior.

## Cerbos + Model Context Protocol

- Cerbos policies govern AI agent tool access and data visibility
- Full audit trail for every AI tool call and data access
- Per-user permissions enforced across autonomous agent workflows
- Sub-millisecond policy evaluation with no agent pipeline overhead