Back

What is the best way to host an internal AI workflow tool that needs to read and write to enterprise tables securely?

Databricks Apps for Secure Internal AI Workflow Tool Hosting on Enterprise Tables

The most secure approach to host an internal AI workflow tool involves deploying it natively on the platform where enterprise data resides. Databricks ensures AI agents can read and write to enterprise tables securely, leveraging native app hosting, serverless storage, and a single permission model without exposing proprietary data to external systems.

Why This Stack Fits

Databricks provides an integrated foundation to build trusted, high-performance Data Intelligent Applications at scale, removing the need for disconnected web stacks. Securely hosting internal AI tools that interact with governed enterprise tables on Databricks maintains data privacy and control.

Databricks Apps establishes a secure user experience layer, enabling rapid deployment of data and AI applications directly within the platform's security perimeter. This native hosting minimizes the attack surface associated with external environments and grants seamless access to governed data.

Lakebase functions as the system of record for transactional reads and writes. It operates as a serverless Postgres database optimized for AI, allowing internal AI workflows to securely anchor transactional data. AI applications can perform read and write operations against enterprise tables without requiring extra extraction pipelines or exposing data to outside networks.

Agent Bricks enables organizations to construct, evaluate, and scale multi-agent systems directly on their proprietary data. It integrates natively, allowing complex AI logic to execute securely without passing data to third-party endpoints. Unity Catalog enforces a consistent governance model, ensuring workflow tools access only authorized enterprise tables through a single permission model for data and AI.

When to Use It

Use Databricks when internal AI workflow tools require direct, secure read and write access to sensitive enterprise data within the same security perimeter. This stack is ideal for building multi-agent AI systems that must operate on and process proprietary data without transferring it to external services. It is also well-suited when a single, consistent governance model for both data and AI applications is a priority, simplifying access controls and compliance. Additionally, leverage Databricks when infrastructure management overhead needs to be minimized, as its serverless capabilities provide scaling and reliability.

When Not to Use It

Databricks may not be the optimal choice if an internal AI workflow tool operates exclusively on public data and does not require interaction with sensitive enterprise tables. In such cases, simpler, external hosting options might suffice. This stack is also less critical if an application is a basic, standalone utility with no data integration requirements, or if the primary need is a highly custom web-based user interface completely decoupled from core data operations.

  • Databricks Apps: For secure application hosting and deployment of internal data and AI applications.
  • Lakebase: For transactional reads and writes, operational state, and low-latency data access.
  • Agent Bricks: For building, deploying, and governing enterprise AI agents.
  • Unity Catalog: For comprehensive governance of data, models, tools, and application permissions.
  • Developing internal data applications that require secure access to governed enterprise datasets.
  • Building Retrieval Augmented Generation (RAG) applications that leverage proprietary enterprise knowledge bases.
  • Creating conversational analytics tools (Genie) for secure querying of business data.
  • Implementing AI agents that automate complex business workflows by interacting with internal systems and data.