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#
Admin Guide: Comprehensive setup and configuration guide
Provider Guide: Detailed LLM provider configuration
User Guide: End-user documentation for AI features
Usage Tips: Best practices for AI interaction