Tag: Security Classification

Generative AI RAG Applications: Protecting Sensitive Data Safely

The Challenge: Lost in Translation

Retrieval-Augmented Generation (RAG) is revolutionising how we access information. By connecting powerful Large Language Models (LLMs) to our private data, we can create incredibly smart assistants. But there’s a hidden security challenge: RAG applications work by shredding documents into hundreds of small chunks. What happens to the “Top Secret” stamp or the “Eyes-Only for the Finance Team” note on the original document?

Without a proper security design, these critical access controls can be lost. A user asking a general question could receive an answer synthesised from a highly sensitive chunk they were never meant to see. In secure environments, this is a non-starter.

So, how do you ensure your RAG system respects your organisation’s “need-to-know” policies?

The answer lies in making your data’s security permissions a first-class citizen throughout the entire AI pipeline.

The Solution: Smart Data with Metadata Filtering

The most robust solution is to embed your access rules directly into your data using metadata. Think of it like a digital security tag attached to every single chunk of information. When a user asks a question, the system first checks their ID card against the security tag on every chunk before even beginning its search. This is metadata filtering, and it’s the gold standard for a secure RAG.

Here are the most common approaches to implementing it.

Option 1: Attribute-Based Access Control (ABAC)

This is the most flexible and powerful approach. Each data chunk is tagged with specific attributes (e.g., caveat: ‘PROJECT_X’, department: ‘LEGAL’), and each user is assigned a set of attributes. The system grants access if the user’s attributes match the data’s attributes.

  • Pros:
    • Extremely Granular: Control access down to a single document for a single user.
    • Highly Scalable: Policies are dynamic and don’t require changing roles for every new project.
  • Cons:
    • Complex Setup: Requires a mature identity management system and careful planning to define all the attributes.

Option 2: Role-Based Access Control (RBAC)

RBAC is a more traditional and often simpler approach. Instead of fine-grained attributes, users are assigned roles (e.g., ‘Project_X_Analyst’, ‘Compliance_Officer’). Each document chunk is then tagged with the roles that are allowed to see it.

  • Pros:
    • Easier Management: Simpler to manage permissions for large groups of users.
    • Intuitive: Often aligns with existing organisational structures and roles.
  • Cons:
    • Less Flexible: Can lead to “role explosion” where you need to create dozens of roles to cover all permissions. Not great for handling one-off “eyes-only” exceptions.

Option 3: Access Control Lists (ACLs)

This is the simplest method, where each document chunk’s metadata contains a literal list of the user IDs or group IDs allowed to view it.

  • Pros:
    • Simple to Implement: Very straightforward logic for small-scale projects.
  • Cons:
    • Doesn’t Scale: Managing lists of thousands of users across thousands of documents is unmanageable and prone to errors. Best avoided for enterprise systems.

How It Works in Practice

Regardless of the model you choose (ABAC is typically best), the implementation follows four key steps:

  1. Tag at the Source: During ingestion, extract the security classifications from each document and attach them as metadata.
  2. Chunk with Inheritance: When you split the document into pieces, ensure every single chunk inherits the parent’s security metadata. This is the most critical step.
  3. Store Together: In your vector database, store the vector embedding and its rich metadata object side by side.
  4. Filter Before Searching: When a user makes a query, the system first verifies their identity and permissions. It then builds a security filter and tells the database, “Only search for answers within the chunks that match this user’s permissions.”

This ensures the LLM only ever sees data the user is already cleared to see, effectively eliminating the risk of accidental data leakage.

Final Summary

Building a RAG application for a secure environment requires treating access control as a core design pillar, not an afterthought. By embedding permissions as metadata and enforcing filtering at the database level, you can build a powerful AI tool that is not only intelligent but also trustworthy and secure. This approach transforms security from a simple gatekeeper into an integral part of the data itself, ensuring your sensitive information stays that way.