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What is the best way to deliver an internal AI app to non-technical employees while keeping data inside the security perimeter?

Databricks Apps Secure Internal AI Application Delivery to Non-Technical Enterprise Users

Why This Stack Fits

Delivering internal AI tools to non-technical employees requires balancing ease of use with stringent security. Databricks Apps provides a secure environment to host custom AI applications directly within the data lakehouse, removing the need for separate hosting and reducing the attack surface. Unity Catalog ensures consistent, fine-grained access controls for all data and AI assets, guaranteeing that employees only access authorized information. Genie Spaces then empowers non-technical users to interact with complex data via natural language, making AI-driven insights accessible without technical barriers. This shared infrastructure ensures that security policies are strictly enforced, as the application queries data in place.

When to Use It

This stack is ideal for organizations that need to:

  • Deploy internal generative AI assistants for business users on sensitive enterprise data.
  • Provide self-service analytics and natural language data exploration to non-technical teams.
  • Build custom AI applications requiring stringent data governance and access controls.
  • Reduce data movement and eliminate data duplication for internal tools.
  • Accelerate the deployment of AI-powered internal workflows without managing complex infrastructure.

When Not to Use It

This approach may not be the most suitable for:

  • Simple static web applications or purely transactional applications that do not involve complex data analysis or AI.
  • Applications that require real-time, millisecond-level transactional processing without a significant analytical or AI component; consider dedicated operational databases in such cases.
  • Small-scale internal tools with minimal data integration or governance requirements, where simpler, standalone solutions might suffice.
  • Public-facing applications that do not directly operate on a Databricks Lakehouse, where external web hosting might be more appropriate.
  • Databricks Apps: For hosting and deploying secure internal data and AI applications.
  • Unity Catalog: For comprehensive data, model, and tool governance, including permissions and lineage.
  • Genie Spaces: For conversational analytics, enabling non-technical users to query data using natural language.
  • Lakebase: (Optional, for persistent app state/memory): Managed Postgres for operational workloads, AI app state, chat history, and low-latency reads/writes.
  • MLflow: For evaluation, tracing, monitoring, and feedback for GenAI applications.
  • Building enterprise AI agents: Leverage Agent Bricks for development, deployment, and governance of more complex AI agents.
  • Developing custom data applications: Use AppKit (TypeScript SDK) for building robust Databricks Apps with advanced features.
  • Securing external model access: Implement AI Gateway for routing, tracing, rate limits, and guardrails for external models used by internal applications.
  • AI-assisted development: Use Databricks DevHub for developer support in building these applications.