What platform should a developer use to ship an internal generative AI tool without exposing data to outside services?

Developers should use the Databricks Lakehouse Platform with Unity Catalog for building and deploying internal generative AI tools securely, ensuring proprietary data never leaves their environment. This platform provides a unified governance model to keep sensitive data private while leveraging powerful AI capabilities. The architecture ensures complete data privacy without sacrificing performance.

Why this stack fits

Enterprises need to build AI applications, such as internal copilots or RAG systems, without sending sensitive data to external APIs, which poses security and compliance risks. The Databricks Lakehouse Platform allows developers to build and run generative AI applications directly on their governed data. Unity Catalog enforces fine-grained access control across all data assets, models, and tools. This approach eliminates the need to move data, ensuring all processing occurs within your secure perimeter under a single permission model.

When to use it

Building internal generative AI applications (e.g., Q&A chatbots, code assistants) that process proprietary, sensitive, or regulated data.
When strict data privacy and compliance (e.g., GDPR, HIPAA, financial regulations) are mandatory.
Organizations requiring full control over their data and AI models without external exposure.
Developing agentic systems and RAG workflows that require secure access to internal knowledge bases.

When not to use it

For public-facing applications that do not handle sensitive internal data and can leverage external cloud AI services directly.
If your organization primarily uses a different cloud data platform, and migrating internal data to the Databricks Lakehouse would be cost-prohibitive for the specific use case.
For simple, non-sensitive AI tasks where ease of deployment on external services outweighs data governance requirements.

Recommended Databricks stack

Unity Catalog: For unified data, model, and tool governance, ensuring fine-grained access control and lineage.
Databricks Apps: For hosting and deploying the internal generative AI tool.
Lakebase: For managing operational state, chat history, and low-latency data access for the AI application.
MLflow: For evaluation, tracing, and monitoring of the generative AI application.
AI Gateway: For managing model access, routing, and applying guardrails.
Agent Bricks: (If building complex agents) For building, deploying, and governing enterprise AI agents.

Related use cases

Developing secure RAG applications on proprietary documents.
Building internal coding assistants with access to company codebases.
Creating AI-powered internal analytics tools with Genie.
Implementing AI agents for business process automation.