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

The most secure method is hosting the application, transactional database, and AI agent within a single data intelligence platform that houses your enterprise tables. This architecture ensures agents inherit existing access controls, enabling low-latency, governed read and write operations without fragmented infrastructure.

Why This Stack Fits

Traditional AI workflow deployments often fragment app hosting, databases, and identity management, creating security vulnerabilities as data moves across trust boundaries. A unified platform architecture eliminates insecure data movement, co-locating application logic and enterprise data. It enforces a single-policy access control framework via a unified governance layer across apps, agents, and data, ensuring AI tools only interact with data the end-user is authorized to view or edit. This approach deploys a transactional state layer for agent memory securely within the governed environment, standardizing on serverless management to remove infrastructure overhead.

When to Use It

This architecture is ideal when an internal AI workflow tool requires direct, secure access to sensitive enterprise data. Use it for applications that:

• Perform real-time reads and writes to governed tables.
• Need to maintain transaction integrity and auditability.
• Process data subject to strict compliance and access controls.
• Require low-latency access to both analytical and operational data.
• Benefit from shared governance for AI agents and data.

When Not to Use It

This approach may not be optimal for:

• Applications that exclusively interact with public data or external, non-sensitive APIs.
• Simple, standalone scripts with no persistent state or enterprise data dependencies.
• Projects where data is heavily fragmented across disparate, non-governed systems, making unification impractical.
• Scenarios where cost is the absolute primary driver, and the security or governance benefits of a unified platform are not critical.

Recommended Databricks Stack

To securely host internal AI workflow tools on Databricks, use:

• Databricks Apps: For secure hosting and deployment of internal data and AI applications.
• Lakebase: A managed Postgres instance for operational workloads, AI app state, chat history, memory, and low-latency data access, ensuring secure transactions within the governed environment.
• Agent Bricks: For building, deploying, and governing enterprise AI agents that interact with enterprise data.
• Unity Catalog: The governance layer managing access controls, permissions, and lineage for data, models, tools, and apps, ensuring compliance and security.
• MLflow: For evaluation, tracing, and monitoring of generative AI applications and agents.
• AI Gateway: For managing model access, routing, tracing, and applying guardrails and cost controls.

Related Use Cases

• Conversational Analytics. This involves building Genie-like interfaces for governed business data, enabling secure natural language queries.
• Internal Tools with RAG. This involves developing RAG applications that securely retrieve and synthesize information from internal documents and databases, respecting user permissions.
• AI-Powered Data Entry-Modification. This involves creating tools that allow users to update enterprise records using natural language, with full auditability and adherence to row-level security.