Forum Discussion
Advocate IIntegrating a Large Language Model with Power BI for Natural Language Queries
Hello everyone,
Does anyone know how to integrate a large language model (LLM) with Power BI to enable natural language querying? but with the requirement of data security like it should not be an open model.
Hi LB_Team
Integrating a Large Language Model (LLM) with Power BI for natural language querying can certainly enhance user interaction, but your concern about data security is valid and important.
When considering methods like Copilot or Azure Machine Learning, it's essential to assess whether the model runs in a secure, tenant-isolated environment and if it supports enterprise-grade compliance standards like data encryption at rest and in transit, identity access management, and data residency controls.
Unlike open models, enterprise integrations via Azure OpenAI or private endpoints within Azure ML allow for more control over data exposure, ensuring that sensitive or confidential information remains secure. Evaluating the architecture and governance settings of these tools is crucial before deployment in environments that handle sensitive data.
4 Replies
- Ritaf1983
Super User
Hi LB_Team
Please relate to the linked discussion :
https://community.fabric.microsoft.com/t5/Desktop/Integrate-Large-Language-Model-LLM-with-Power-BI/td-p/3598731If this post helps, then please consider Accepting it as the solution to help the other members find it more quickly
- Ritaf1983
Hi LB_Team
I don't think information security is something that can be properly evaluated within the forum.
These matters are usually reviewed with Microsoft architects, typically through local integration partners in your country.If this post helps, then please consider Accepting it as the solution to help the other members find it more quickly
rohit1991Hi LB_Team
Integrating a Large Language Model (LLM) with Power BI for natural language querying can certainly enhance user interaction, but your concern about data security is valid and important.
When considering methods like Copilot or Azure Machine Learning, it's essential to assess whether the model runs in a secure, tenant-isolated environment and if it supports enterprise-grade compliance standards like data encryption at rest and in transit, identity access management, and data residency controls.
Unlike open models, enterprise integrations via Azure OpenAI or private endpoints within Azure ML allow for more control over data exposure, ensuring that sensitive or confidential information remains secure. Evaluating the architecture and governance settings of these tools is crucial before deployment in environments that handle sensitive data.
