Authorize LlamaIndex agent actions and data retrieval with Cerbos
Enforce policy-driven authorization on LlamaIndex agent tool calls and data connector access using Cerbos.
Tool and data authorization
Control which LlamaIndex tools and data connectors each user or role can invoke with fine-grained Cerbos policies
Context-aware decisions
Authorize agent actions based on user identity, roles, attributes, and request context at runtime
Audit every agent action
Every authorization decision is logged with full context, giving you a complete audit trail of agent behavior
How Cerbos works with LlamaIndex
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 LlamaIndex, 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 authorizes LlamaIndex agents
- Define policies for tool and data access, Write YAML policies that specify which tools and data connectors each user or role can invoke based on identity, attributes, and context.
- Agent requests authorization before each action, Before invoking a tool or querying a data connector, the application sends the user context, action, and target resource to the Cerbos PDP.
- Cerbos evaluates the request against policies, The PDP applies fine-grained rules considering the user's identity, role, and any additional attributes you provide.
- Action proceeds or is blocked, Cerbos returns an allow or deny decision. The application enforces it, with every decision logged for audit.
FAQ
How does Cerbos authorize LlamaIndex agent actions?
Before a LlamaIndex agent invokes a tool or queries a data connector, the application sends the user context, action, and target resource to the Cerbos PDP. Cerbos evaluates fine-grained policies and returns an allow or deny decision.
Can I restrict which data connectors an agent can access?
Yes. Cerbos policies are attribute-based, so you can restrict data connector access by role, department, data classification, or any other context you provide. Policies are written in YAML and managed outside your application code.
Does this work with LlamaIndex RAG pipelines?
Yes. Cerbos query plans can be translated into metadata filters for vector stores, ensuring retrieval-augmented generation only returns documents the requesting user is authorized to access.
Learn more about Cerbos
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Cerbos + LlamaIndex
- 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
Authorization for AI systems
By industry
By business requirement
Useful links
What is Cerbos?
Cerbos is an end-to-end enterprise authorization software for Zero Trust environments and AI-powered systems. It enforces fine-grained, contextual, and continuous authorization across apps, APIs, AI agents, MCP servers, services, and workloads.
Cerbos consists of an open-source Policy Decision Point, Enforcement Point integrations, and a centrally managed Policy Administration Plane (Cerbos Hub) that coordinates unified policy-based authorization across your architecture. Enforce least privilege & maintain full visibility into access decisions with Cerbos authorization.