Implementing Sovereign AI

Overview

Sovereign AI is an approach to artificial intelligence implementation that prioritizes organizational control over data, processing, and infrastructure. In the context of Mattermost, this is realized through the Agents plugin, which enables organizations to leverage advanced AI capabilities through LLMs while maintaining complete control of their sensitive communications and information.

Sovereign AI has become increasingly critical as organizations seek to balance AI-enhanced productivity with stringent data sovereignty requirements. Mattermost’s implementation through the Agents plugin enables organizations to maintain control over their data location, processing methods, and access controls throughout the AI interaction lifecycle.

Understanding Sovereign AI in Mattermost

Sovereign AI in Mattermost encompasses several key principles:

Data Sovereignty

Organizations maintain complete control over their data, including where it resides, how it’s processed, and who can access it.

Infrastructure Control

Through the Agents plugin, organizations can choose their preferred LLM backend implementation, whether it’s cloud-based services like OpenAI, AWS Bedrock, and Azure AI, or self-hosted models running on their infrastructure.

Extensible Architecture

The plugin ecosystem supports integration with various data sources and services through MCP (Model Context Protocol), allowing organizations to incorporate their existing tools and data repositories into the AI workflow.

Mattermost Agents Plugin Architecture

Core Components

The Mattermost Agents plugin serves as the foundation for Sovereign AI implementation, offering:

LLM Backend Flexibility

  • Self-hosted LLM deployment options

  • Integration with managed cloud provider LLMs (AWS Bedrock, Azure AI)

  • Support for cloud-based API services (OpenAI, Anthropic)

  • Custom LLM integration capabilities through OpenAI-compatible APIs

Multi-Bot Architecture

  • Support for multiple concurrent AI bots

  • Different LLM backends per bot (e.g., OpenAI and self-hosted LLM simultaneously)

  • Customizable bot configurations, instructions, and capabilities

  • Independent access controls per bot

Vector Search Capabilities

  • Self-hosted vector database options using PostgreSQL with pgvector

  • Semantic search across organizational content

  • Respects existing permission structures

Data Integration Framework

  • Integration with external tools through built-in connectors (GitHub, Jira)

  • MCP server support for custom integrations

  • Tool approval system for security

Security and Compliance Features

Fine-grained Access Controls

  • Team-level, channel-level, and user-level permissions for each bot

  • Role-based access to specific AI features

  • Granular control over tool usage and integrations

Private Environment Deployment

  • Support for air-gapped environments

  • Government cloud compatibility

  • On-premises deployment options

  • Network isolation capabilities

Data Privacy Controls

  • Optional user ID transmission to LLM providers

  • Conversation data stays within Mattermost infrastructure

  • Audit trails for AI interactions

  • Configurable data retention policies

Deployment Patterns

Full Self-Hosted Implementation

For organizations requiring complete data sovereignty, the Agents plugin supports fully self-hosted deployments:

  • Deploy models like Llama, Mistral, or other open-source models on your infrastructure

  • Use PostgreSQL with pgvector extension for semantic search capabilities

  • Support for completely disconnected and air-gapped environments

  • MCP servers for connecting to internal systems and custom integrations

Hybrid Cloud Infrastructure Deployment

Organizations can leverage cloud infrastructure while maintaining control by deploying on platforms like AWS Bedrock, Azure OpenAI Service, or Oracle Cloud Infrastructure. These deployments can use managed LLM services while maintaining private network connectivity, custom compute instances for self-hosted models, and integration with existing cloud identity and access management systems.

Public LLM Integration with Data Controls

For organizations comfortable with cloud LLM services but requiring data controls:

  • API-only interactions with no data storage at external providers

  • Configurable data transmission to control what information is sent to LLMs

  • Session isolation ensuring each conversation is independent

  • Comprehensive audit logging to track all AI interactions

Additional Resources