This C# system addresses the critical need for managing sensitive data used in Large Language Model (LLM) grounding. It provides developers with a robust framework for data protection and governance throughout the AI agent development lifecycle, ensuring compliance and security for sensitive information.

How It Works

The system employs a layered architecture comprising a Core Library, CLI Application, Agent Integration utilities, and a Configuration module. Its core approach centers on a configurable sensitivity label hierarchy (e.g., Public, Internal, Confidential, Restricted), allowing granular control over data. Each label can enforce specific protection policies, including mandatory encryption and restrictions on data extraction or grounding, thereby maintaining data integrity and privacy.

Quick Start & Requirements

Installation involves cloning the repository (git clone https://github.com/your-org/LLMSensitiveDataGoverance.git), navigating into the directory (cd LLMSensitiveDataGoverance), and building the project (dotnet build). Basic usage can be initiated via the CLI (dotnet run --project src/LLMSensitiveDataGoverance.CLI -- classify --content "sensitive data") or integrated into C# applications using services.AddSensitivityLabelServices(). The primary requirement is .NET 8.0 or later; no external dependencies are needed for core functionality.

Highlighted Details

• Implements privacy-compliant sensitivity labels with integrated encryption and access controls.
• Features visual indicators for label status within AI responses and copy/paste protection that preserves label integrity.
• Enforces grounding data protection based on defined sensitivity levels and a customizable label hierarchy.
• Designed for local, standalone operation, eliminating network dependencies for core governance functions.

Maintenance & Community

The repository outlines a standard contribution process via pull requests. No specific details regarding active maintainers, community channels (like Discord or Slack), sponsorships, or a public roadmap are provided in the README.

Licensing & Compatibility

This project is licensed under the permissive MIT License. This license generally allows for broad compatibility, including commercial use and integration within closed-source applications without significant restrictions.

Limitations & Caveats

The system is tailored for the .NET ecosystem and operates locally, suggesting it is not a cloud-native solution. The README does not detail specific performance benchmarks, advanced integration patterns beyond basic examples, or potential scalability limitations for very large-scale deployments.