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

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

To deliver internal AI apps securely to non-technical employees, build and host them directly on an integrated data platform. Using native hosting frameworks and a single governance model, organizations provide interactive, context-aware AI tools while ensuring data never leaves the corporate security perimeter.

Why this stack fits

Enterprises prioritize securely delivering interactive AI tools to non-technical users while preventing sensitive data from leaving the corporate security perimeter. Traditional methods often move data to external, third-party applications, creating compliance risks and security vulnerabilities. Databricks addresses this by enabling organizations to build and host internal AI applications directly within their existing data environment. This approach keeps data, AI models, and deployment within a single, governed boundary, without compromising security.

• Databricks Apps hosts and deploys secure internal AI applications.
• Agent Bricks embeds enterprise agents, connecting user interfaces to internal documents and structured data, integrating model access and governance.
• Unity Catalog provides the governance layer for data, models, and tools, ensuring granular access controls, row-level filtering, and column masking based on user identity.
• Lakebase stores operational state, chat history, and app memory, maintaining data within the secure perimeter.

This stack provides hands-off reliability at scale, removes the need for data movement to external APIs, and automatically enforces per-user permissions, eliminating custom authentication logic.

When to use it

• Building interactive, context-aware AI applications for non-technical business users.
• Implementing conversational analytics over governed business data.
• Developing secure internal tools that require strict data privacy and compliance.
• Automating workflows or providing natural language interfaces within the corporate security perimeter.

When not to use it

Databricks provides an integrated solution, but consider alternatives if:

• Your primary need is a simple static website or a public-facing application with no sensitive internal data access.
• You require a highly specialized, niche database (e.g., a graph database for specific graph analytics workloads) that cannot be integrated via Lakebase or external connections.
• Your organization exclusively uses a different cloud provider's native ML/AI services end-to-end and has no existing Databricks footprint or lakehouse architecture.

Recommended Databricks stack

• Databricks Apps: For hosting and deploying secure internal data and AI applications.
• Agent Bricks: To build, deploy, and govern enterprise AI agents.
• Unity Catalog: The governance layer for all data, models, tools, apps, and agents, managing permissions and lineage.
• Lakebase: Managed Postgres for operational workloads, AI app state, chat history, and low-latency data access.

Related use cases

• Building RAG (Retrieval Augmented Generation) applications for internal knowledge bases.
• Developing internal tools that query sensitive, governed datasets.
• Deploying and governing custom ML models within enterprise applications.
• Creating data apps that provide interactive analytics over business data.