Why is AI sometimes a risk for your data?
Generative AI models like ChatGPT or Claude are often hosted by large American tech companies. If you use these models through their public platforms, you send your input – such as texts, documents, or customer data – to servers outside your own organization. You are not always sure then:
- Where exactly your data is stored
- Who has access to it
- What happens to your data after the session ends
Although companies like OpenAI say they do not use data for training in paid accounts, it remains a matter of trusting promises. In regulated sectors such as government or healthcare, that is simply not enough.
A healthcare institution we guide, for example, wanted to use AI to summarize multidisciplinary patient discussions. They rightly wondered: "Can we do this safely without exposing medical data to unknown systems?"
The solution: Private AI
With Private AI, you maintain full control over your data. You use the power of an AI model, but without data leaving the secure walls of your organization. This means:
- You decide where your data is processed – for example, in a European Azure environment or on your own servers
- The AI acts as a processor, not as the owner of your data
- You prevent your data from being reused for model training
There are currently two robust approaches that we at PrudAI successfully apply:
1. Azure OpenAI – a trusted cloud, with clear rules
Azure OpenAI offers access to powerful GPT models, but hosted within your own Azure tenant. You then have:
- Data storage within the EU (optional)
- Transparent processing agreement according to European GDPR standards
- No data training on your input
A local government we guide uses Azure OpenAI for automatically summarizing internal documents. The documents are never sent to a third party, and the prompts are logged within their own infrastructure – fully compliant with applicable privacy regulations.
2. Open Source LLMs on own servers – maximum autonomy
For organizations with higher security requirements or more technical capacity, an open source solution is interesting. You then run a model like LLaMA 3, Mistral, or DeepSeek on your own infrastructure or in a private cloud environment. This way, you determine:
- Who has access to the models
- What logging is maintained
- How and when updates take place
Since running your own LLMs brings specific expertise, this option can also be outsourced to PrudAI. We support the hosting of LLMs on own servers located in a data center in Germany. Users have direct and secure access without the management burdens.
And what does it yield?
Many organizations, especially in the public domain, use AI very limitedly or not at all for safety reasons. And that's a shame because AI can fantastically support any organization in quickly handling large amounts of unstructured and complex data. By making AI the beating heart of the organization and deploying agents to perform work, solutions arise for the shortage of staff and the upcoming retirement outflow of older employees.
Conclusion: safely and smartly getting started with AI
AI is not inherently unsafe. It's about how you organize it. With Private AI, you keep control, decide where your data stays, and prevent dependence on external parties.
Whether you choose Azure OpenAI or an open source model on your own or PrudAI server: with the right setup and support, you make AI safe, scalable, and future-proof. At PrudAI, we help organizations find that balance between innovation and control.
Because only then does AI work – for you.