Building AI Applications with Enterprise-Grade Security Using RAG and FGA

This post is written by and Retrieval Augmented Generation (RAG) are effective strategies for building secure, context-aware AI applications that maintain strict access control. In this article, we’ll explore how FGA and RAG can be applied in a healthcare setting while safeguarding sensitive data.

We’ll do this by guiding you through implementing a , .

Use Case Example: Healthcare Applications

To better understand the complexity of authorization in LLM applications, and the solutions offered by FGA and RAG, we can look at the , where permissions are assigned directly.

The power of ReBAC lies in how permissions are derived through these relationships. Let’s look at a visual representation of our example:

In our implementation, authorization checks should happen at a crucial moment - after retrieving data from the vector database but before providing it to the LLM as context. This way, we maintain search efficiency by first finding all relevant information and later filtering out unauthorized data before it ever reaches the LLM. The LLM can only use and reveal information the user is authorized to see.

These security checks are implemented using . Once your free account is created, you'll have access to Permit's dashboard, where you can set up your authorization policies, manage users and roles, and integrate Permit into your applications. The free tier offers all the necessary features to create a digital healthcare example with relationship-based access control (ReBAC).

Both LangFlow and Permit offer free accounts to start work, so you don’t have to pay anything to build such a system and see how it works for yourself.

Implementation Guide

Before we dive into the implementation details, it's important to understand the tool we'll be using - Langflow. Built on top of LangChain, Langflow is an open-source framework that simplifies the creation of complex LLM applications through a visual interface. LangChain provides a robust foundation by offering standardized components for common LLM operations like text splitting, embedding generation, and chain-of-thought prompting. These components can be assembled into powerful pipelines that handle everything from data ingestion to response generation.

What makes Langflow particularly valuable for our use case is its visual builder interface, which allows us to construct these pipelines by connecting components graphically - similar to how you might draw a flowchart. This visual approach makes it easier to understand and modify the flow of data through our application, from initial user input to the final authorized response. Additionally, Langflow's open-source nature means it's both free to use and can be extended with custom components, which is crucial for implementing our authorization checks.

Our Langflow solution leverages two distinct yet interconnected flows to provide secure access to medical information:

1. Ingestion Flow

The ingestion flow is responsible for loading diagnoses into AstraDB along with their respective embeddings. We use MistralAI to generate embeddings for each diagnosis, making it possible to perform semantic searches on the diagnosis data later. The key components involved in this flow are:

• 
  Create List: This component is used to create a list of diagnoses to ingest into AstraDB.


• 
  MistralAI Embeddings : This component generates embeddings for each diagnosis, which are stored in AstraDB.


• 
  AstraDB : AstraDB serves as the vector store where the diagnoses and their embeddings are stored for further retrieval.
  
  

2. Chat Flow

The chat flow is responsible for interacting with users and serving them the required diagnosis data. The images below are supposed to be read from left to right (the right side of the first one continues as the left side of the second one):

💡 Note: There is an additional “_ Pip Install” _ component that is executed only once to install permit module. This is because we are implementing LangFlow on DataStax low-code platform. This step is equivalent to executing pip install permit locally.

The sequence of operations in the Chat Flow is as follows:

1. 
   User Input : The user initiates the interaction by typing a query.

• Example: "Do we have any patients with diabetes diagnosis?"

1. 
   Retrieve Diagnoses : AstraDB is queried for relevant diagnoses based on the user's input.

• Example search result (marked with 1 on the flow image above):

1. 
   Generate Response : Once filtered, the permitted diagnoses are used as the context to generate a response for the user prompt using MistralAI.

• Example prompt with context filtered with authorization step:
  

CODE Kopieren

Seasonal Migraine

Flu virus with high fever



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You are a doctor's assistant and help to retrieve information about patients' diagnoses.

Given the patients' diagnoses above, answer the question as best as possible.

The retrieved diagnoses may belong to multiple patients.



Question: list all the recent diagnoses



Answer: 

PermitFilter Component

To run the PermitFilter component, which plays a crucial role in our implementation, we need a running instance of Permit's Policy Decision Point (PDP). The PDP is responsible for evaluating policies and making decisions on whether a given action is permitted for a specific user and resource. By enforcing this permission check before the context reaches the language model, we prevent the leakage of sensitive information and ensure the enforcement of access control policies.

See It In Action

The complete implementation is available in our integration code

Detailed setup instructions

Example queries and responses

To start interacting with our AI assistant with authorization checks implemented we can simply start the LangFlow playground. In the example below, I am authenticated as [email protected] which means I have access to only Afternoon-Visit and Evening-Visit without Morning-Visit with Diabetes. This means that the LLM does not have the information about diabetes in its context.

, healthcare providers can implement relationship-based access control systems that respond dynamically to role and relationship changes, ensuring data is accessible only to authorized individuals. By integrating these solutions, healthcare organizations can effectively harness AI to improve patient care without compromising on security.

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