Artificial intelligence is revolutionizing the way we work – not only in general, but also in specific contexts: automatic issue summaries, intelligent prioritization, semantic search – these are just a few activities that boost productivity and are therefore also in demand in project management.
The use of AI here is practical, but not without concerns. Anyone working in Germany or the EU faces a challenge: leading AI services such as ChatGPT or Claude process data in the United States – legally problematic under the GDPR and the Schrems II ruling (C-311/18 “Schrems II”, CJEU). This is particularly risky when dealing with sensitive project data from government agencies, healthcare, or regulated industries.
In this article, you’ll learn what options exist for integrating AI into Redmine in a GDPR-compliant way – without your data leaving Germany.
Why This Matters for IT Decision-Makers and Project Managers
GDPR violations can be costly. Handling them takes time and nerves, and often comes with a loss of trust from those affected. But what many overlook: when you send project data such as customer names, internal processes, or personnel data to US cloud services, you lose control over what actually happens to that data – how it is used, and whether you can trust the provider not just today, but in the future as well.
The Schrems II ruling by the CJEU showed that Standard Contractual Clauses (SCCs) are often insufficient when US authorities can demand access under the CLOUD Act.
That said, EU-based companies are not powerless: with the right architecture, you can use AI features such as automatic analyses and intelligent suggestions without legal risks.
GDPR Compliance: The 3 Critical Points
A GDPR-compliant AI integration must fulfill three requirements:
1. Data Residency in Germany/EU Redmine data and AI processing must remain in Germany or the EU. This means: either hosting with a German provider or self-operated servers in German data centers.
2. Data Processing Agreement (DPA) If you use external AI APIs, you need a DPA that governs data processing. For fully local solutions like Ollama, this point is irrelevant – the data never leaves your system.
3. Deliberate Provider Selection Not all AI providers are equal. Many well-known providers process data in the US; Azure OpenAI can use EU regions (GDPR-compliant with DPA); OpenAI recently started offering data residency in Europe for certain customer groups; and local solutions like Ollama remain entirely within your infrastructure (100% GDPR-compliant).
Ollama: The On-Premises Solution for 100% GDPR Compliance
LLMs are compute-intensive applications. There are also open-source LLMs that, with the right hardware, offer capabilities nearly on par with public AI services. Ollama is an open-source platform that runs large language models such as LLaMA, Mistral, or Qwen locally on your servers.
All data stays in Germany. No external transfer, no cloud dependency.
Provider Overview
| Provider | GDPR Status | Data Processing | AlphaNodes Offering | Use Case |
|---|---|---|---|---|
| Ollama (On-Premises) | 100% compliant | Germany/EU, local | Redmine AI Hosting Cloud or On-Premise | Sensitive data, maximum control |
| Azure OpenAI (EU) | Compliant with DPA | EU data center | Redmine AI via Enterprise Support or RE Enterprise Cloud | Cloud infrastructure, scalability |
| OpenAI / Anthropic | Disputed | USA (Schrems II issue) | Redmine AI via Enterprise Support or RE Enterprise Cloud | Test/development environments |
AlphaNodes Managed Ollama: Managed Ollama Infrastructure on Dedicated Hardware
AlphaNodes offers Ollama as a fully managed service on its own servers in Germany. If you book our Redmine AI Hosting Cloud package, you get your own server – meaning your data resides exclusively on your hardware and is not shared with other customers.
What this means for you:
- Your own server exclusively for your project data
- All data stays in Germany
- AlphaNodes handles everything: LLM installation, maintenance, updates, monitoring
- 100% GDPR-compliant – your data never leaves Germany
Popular LLM models include Llama (Meta), Mistral, or Qwen – depending on your requirements and use case.
Your benefits:
- No need to purchase or operate your own server hardware
- No IT team required for server operations
- Fixed monthly costs
- Professional hosting in German data centers
- AI hosting specifically tailored to Redmine – optimally integrated into your Redmine workflows through the AlphaNodes AI Plugin
Getting started is straightforward: Once the Redmine AI package is ready, you configure the Redmine AI Plugin to your needs: define which AI features are used and who can access them (e.g., AI prompts, AI assistant, MCP). That’s it – you can now use AI with your Redmine data. A set of ready-made AI prompt templates is available to get you started quickly. You can customize and extend them at any time.
AI Prompts: A prompt is a predefined instruction to the AI – for example, “Summarize this issue” or “Suggest a priority”. In the Redmine AI Plugin, you can save such prompts and apply them to any issue with a single click. This saves time on recurring tasks. Any team member can use them, regardless of their technical background.
MCP (Model Context Protocol): MCP is an interface that allows the AI to communicate with external tools. For example, the AI can access Slack, GitHub, or other services via MCP to retrieve information or take action – all controlled from within Redmine.
Real-World Use Cases in Daily Project Work
It’s not about using AI for AI’s sake. The question is: which time-consuming tasks can AI take over, so your team can focus on the actual project work? Here are six proven use cases:
1. Automatic Issue Summaries Your support team receives dozens of issues with long descriptions every day. The AI Plugin reads the text and creates a short, easy-to-understand summary. Result: faster assessment, consistent language, less reading time.
2. Intelligent Prioritization The AI analyzes urgency, impact, and category and suggests a priority (high, medium, low). This standardizes prioritization, reduces human error, and speeds up processing.
3. Automatic Classification and Tagging Is it a bug, a feature request, or a support inquiry? The AI automatically detects the category and adds matching tags. This improves clarity and enables automatic routing to the responsible teams.
4. Reply and Solution Suggestions Does your support team frequently write similar replies? The AI generates comment suggestions based on similar, already-resolved issues. This saves time, ensures consistent responses, and accelerates issue closures.
5. Reporting and Analysis With large numbers of issues, things get hard to track. The AI automatically generates reports: number of unresolved issues per category, average resolution time, frequently occurring problem types. This enables faster strategic decisions and helps identify weaknesses in your processes.
6. Semantic Search (with RAG) Vector-based search finds semantically similar issues – not just by keywords, but by meaning. This helps avoid duplicates and reuse proven solutions. Prerequisite: PostgreSQL with pgvector (since all our Redmine Hosting customers use PostgreSQL, this feature is always available).
RAG (Retrieval Augmented Generation): RAG extends the AI with your own data. The AI searches your Redmine issues and wiki pages and uses this information for better answers. This way, the AI knows your projects, past solutions, and project-specific details – rather than relying solely on general knowledge.
Conclusion: GDPR-Compliant and Future-Ready
The optimal combination for GDPR-compliant AI in Redmine is: Managed Redmine Hosting in Germany + Ollama server (operated by AlphaNodes) + AI Plugin. This keeps all data in Germany, makes you independent of external cloud services, gives you flexible AI features, and means you don’t have to manage your own GPU server. Relevant for every organization that wants to meet data protection requirements – and a must for government agencies, healthcare, and regulated industries.