Announcing Permit.io AI Access Control: AI Identity FGA

Today, we’re excited to announce Permit.io AI Access Control—a major step toward securing AI-powered applications with fine-grained identity management.

To secure AI-driven applications with the power of Permit.io’s Fine-Grained Authorization, we are introducing the new Four-Perimeter Framework—a structured approach to securing AI interactions at every stage, from prompt input to final response.

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Consisting of Prompt Filtering, RAG Data Protection, Securing External Access, and Response Enforcement, this framework allows developers to secure AI identity, enforce dynamic permissions, and prevent unauthorized actions—all with integrations that work across any AI stack.

The Four-Perimeter Framework is enabled through a set of four new integrations:

LangChain – Enable authentication, authorization, and data filtering for controlling RAG queries.

LangFlow – Design and orchestrate AI agent access workflows with LangFlow’s no-code visual builder.

PydanticAI – Enforce structured prompt validation and prevent unauthorized inputs/outputs.

MCP (Model Context Protocol) – Enforce identity-based access control before AI models execute actions through integration with MCP’s Servers.

Why AI Needs Access Control & Identity Security

AI applications process vast amounts of data and execute automated actions. Without proper access control, they become vulnerable to unauthorized access, data leaks, and compliance risks.

Fine-grained authorization (FGA) ensures that only the right users—and AI agents operating on their behalf—can access and act on data, reducing the risk of AI misuse, prompt injections, and sensitive data exposure.

Permit.io AI Access Control provides a structured and scalable way to implement identity-based security into AI-powered applications—without adding unnecessary friction to development.

Permit.io’s Four-Perimeter Framework

Securing AI is about more than just permissions—it requires controlling inputs, actions, data access, and responses. The Four-Perimeter Framework ensures security at every stage:

Prompt Filtering –

Prompt Filtering ensures that only authorized and validated inputs reach AI models, reducing the risk of manipulation and security breaches. It enforces strict prompt policies for both user and system prompts, which prevent AI jailbreaks while blocking harmful user inputs, such as SQL injections and prompt-based attacks.

By applying granular, attribute-based access policies—based on role, subscription tier, or organization—developers can restrict prompt usage according to predefined criteria. Additionally, real-time enforcement dynamically validates and authorizes prompts before they reach the AI model, ensuring that only permitted requests are processed.

RAG Data Protection –

RAG Data Protection ensures secure and controlled access to AI knowledge bases and vector databases by enforcing fine-grained filtering at multiple levels. It defines who can retrieve specific data through applying relationship-based access control (ReBAC) RAG queries. Implementing both pre-query and post-query filtering restricts data access before retrieval and removes sensitive information from processed results.

Seamlessly integrating with chain and agentic frameworks, Permit.io enables fine-grained authorization (FGA) on AI data retrieval, ensuring that AI agents only access and utilize permitted information.

“Building AI Applications with Enterprise-Grade Security Using RAG and FGA” provides a practical example of RAG data protection for healthcare.

Secure External Access –

Enforcing identity-based permissions for AI-driven operations is another crucial part of ensuring your AI agents can only operate within pre-set boundaries in a controlled and auditable manner. By assigning machine identities to AI agents, developers can track and manage their access to APIs, databases, and third-party services while defining precisely which actions they are authorized to perform.

Critical operations, such as purchases or account modifications, can require a "human in the loop" able to approve operations through structured workflows, preventing unauthorized transactions. With dynamic approval flows and embeddable access request interfaces, developers can maintain strict oversight while enabling AI to act on behalf of users.

This capability is further enhanced through Permit.io’s ability to retain a full image of each request’s origin and the full chain of “On-Behalf” access requests with detailed, auto-generated audit logs.

Response Enforcement –

Response Enforcement ensures AI-generated outputs remain safe, compliant, and contextually appropriate by applying content moderation and role-based access controls. It allows developers to set rules that limit the usage of sensitive or inappropriate information in the AI’s output before responses are delivered, preventing unintended data exposure.

Output control allows developers to define what different users can see, restricting certain AI-generated content based on user roles or permissions. This approach ensures that AI responses remain transparent, controlled, and aligned with security and compliance requirements.

Permit.io’s Four-Perimeter Framework provides a comprehensive approach to securing AI interactions, ensuring that inputs, data access, external operations, and responses are all tightly controlled.

The Four AI Access Control Integrations

Building on the Four-Perimeter Framework, Permit.io’s new AI Access Control integrations bring these security principles into practical, developer-friendly solutions.

LangChain + Permit.io

The Permit.io + LangChain integration enhances LangChain’s AI workflows by introducing identity-based authentication and granular permission control.

Through JWT validation, AI agents must authenticate before executing actions, ensuring that only verified entities can operate within the system. The permit.check function allows developers to enforce access policies at key decision points, preventing unauthorized data retrieval or execution.

The integration also secures RAG implementations by leveraging LangChain’s retrievers to filter RAG results based on fine-grained authorization policies, preventing unauthorized access to sensitive information.

LangFlow + Permit.io

Permit.io’s integration into LangFlow allows developers to implement fine-grained access control directly into AI agent workflows using a no-code visual editor. By adding permission check nodes to LangFlow’s interface, users can define and enforce security policies at each step of the AI decision-making process.

This integration also enables combined data validation and authorization workflows, ensuring that only verified and permitted data moves forward within AI pipelines. It also allows developers to assign machine identities to AI agents, defining their operational boundaries and preventing unauthorized actions.

As Gabriel Almeida, Founder & CTO @ LangFlow mentioned on this integration:

"Having this as a component within LangFlow, where you can quickly filter data and control where data is retrieved from, is incredibly valuable. You don't have to code anything—it just works out of the box. It's a very good addition to LangFlow as a whole."

PydanticAI + Permit.io

The integration between Permit.io and PydanticAI enhances secure prompt validation and structured data handling in AI-driven applications.

PydanticAI is a Python agent framework designed to simplify the development of production-grade Generative AI applications. By embedding Permit.io’s FGA capabilities into PydanticAI’s validation pipeline, developers can ensure that only authorized and structured inputs reach AI models.

With this integration, developers can define and enforce role, attribute, and relationship-based access control (RBAC, ABAC, ReBAC) directly within PydanticAI workflows. Unauthorized parameters can be blocked in real time, preserving an auditable trail of access decisions.

MCP + Permit.io

Permit.io’s integration with the Model Context Protocol (MCP) enables AI models to interact securely with external tools and services. MCP structures AI interactions through a server-client model, where AI agents request access to external systems such as databases, APIs, or payment platforms. Permit.io introduces authorization rules at the server level, verifying who is making the request and what action they intend to perform before granting access.

By assigning machine identities to AI agents, developers can limit their capabilities to pre-approved functions, ensuring that AI-driven operations remain accountable, traceable, and secure.

The Future of AI Identity & Security

As AI applications become more complex, AI identity management is critical to ensuring security, compliance, and responsible AI behavior. We hope that Permit.io AI Access Control will help lay the foundation for secure AI interactions, bringing identity-aware authorization to every stage of the AI lifecycle.

These integrations are just the beginning—our goal is to make AI security as seamless as possible for developers, so stay tuned!

We invite you to try out these new integrations and start enforcing fine-grained access control in your AI applications. If you have any questions, make sure to join our Slack community, where thousands of devs are building and implementing authorization.