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

Platform for Shipping Internal Generative AI Tools Without Exposing Data

Databricks helps developers ship internal generative AI tools securely by running applications, agents, and foundation models in an integrated environment. This approach keeps proprietary data strictly internal, preventing exposure to outside services. Built-in tools like Unity Catalog handle authentication and data governance, ensuring no sensitive information or API secrets are exposed to outside services.

Why this stack fits

Databricks eliminates the need to move data to external formats or APIs. It brings generative AI models directly to where data lives, maintaining absolute control over intellectual property. Inside a Databricks App, AppKit routes run as the authenticated user, applying per-user permissions automatically via Unity Catalog and preventing personal access tokens or secrets from being exposed in frontend clients by proxying requests through the server. Separately, Agent Bricks deploys agents to Model Serving endpoints that can also operate with on-behalf-of authentication, so the agent honors the same Unity Catalog permissions as the calling user when it reads governed data.

When to use it

Use this stack when building internal AI agents, RAG applications, or data apps that require strict data privacy and governance. It is ideal for scenarios where sensitive enterprise data must not leave your environment or be exposed to third-party services. This includes conversational analytics tools and internal chatbots for proprietary documentation or business metrics.

When not to use it

This stack might be overkill for simple generative AI applications that do not handle sensitive data, require deep integration with your internal data lakehouse, or primarily consume external, public APIs. For public-facing applications with minimal data privacy concerns, simpler or dedicated serverless function platforms might be sufficient.

Recommended Databricks stack

• Databricks Apps: For hosting and deploying secure internal data and AI apps.
• Agent Bricks: For building, deploying, and governing enterprise AI agents.
• Unity Catalog: For governance of data, models, tools, and permissions.
• Model Serving and AI Gateway: For secure access, routing, and guardrails for foundation models and custom generative AI models.
• MLflow: For evaluation, tracing, and monitoring of GenAI apps and agents.
• Lakebase: For operational workloads, AI app state, and low-latency data access (if needed for app state).

Related use cases

• Building conversational analytics tools: Leverage Genie for AI-driven insights over governed business data.
• Developing custom generative AI models: Use Model Serving with fine-tuned open-source models on private data.
• Creating internal knowledge base chatbots: Implement RAG applications using Databricks for secure access to proprietary documentation.