u/jaouanebrahim

Hi everyone,

Disclosure: I work on an open-source digital workplace platform.

I’ve been looking into how organizations can safely integrate AI assistants into enterprise collaboration environments (documents, tasks, intranet, knowledge bases, etc.) without breaking existing permission models or exposing sensitive data.

One approach we’ve been experimenting with is using an MCP (Model Context Protocol) server to expose internal platform capabilities (e.g. content access, actions, workflows) to AI assistants in a controlled way.

Some of the key design considerations we’ve run into:

• How to enforce existing ACLs consistently when AI systems query internal data
• Whether OAuth is sufficient for securing AI-to-platform interactions
• How to limit AI context (e.g. per space, project, or document scope)
• Auditability of AI-driven actions in enterprise systems
• Balancing multi-LLM support (cloud vs self-hosted models)

I’m curious how others here are approaching similar problems.

• Are you experimenting with MCP or similar patterns internally?
• How do you currently integrate AI with internal tools securely?
• What governance or control mechanisms are you using for AI access to enterprise data?

Would be interested in hearing real-world approaches or lessons learned.

A new release of eXo Platform, an open-source digital workplace platform, is now available. It may be relevant to the self-hosted community here.

With version 7.2, the focus has been placed on three main areas:

• Native AI integration directly inside the platform (content management, knowledge access, collaboration, automation)
 • Multi-LLM architecture → use the AI models you choose (OpenAI, local models, private deployment, etc.)
 • Full deployment flexibility → cloud, private cloud, or fully on-premise/self-hosted

A few technical highlights:

• MCP server exposed via OAuth with access to 100+ platform tools for AI agents
 • Internal RAG connected to organizational knowledge bases
 • Ability to restrict/contextualize AI sources (documents, spaces, tasks, notes…)
 • AI assistants that can be customized for specific internal workflows
 • Open-source architecture designed for organizations requiring data sovereignty

The goal is simple: integrate AI into everyday work without forcing organizations into closed SaaS ecosystems.

Feedback from people building self-hosted alternatives in this space is welcome.

Curious how others here are approaching AI + self-hosting.

eXo offers:

• Community Edition (CE) → Fully Open Source
  • Docker hub
  • Github
• Enterprise Edition (EE) → additional features & professional support

Both can be deployed self-hosted, in private cloud, or in secure environments (including SecNumCloud).