Databricks Clean Rooms vs Ubiq

Executive Summary

Databricks Clean Rooms are useful for governed data collaboration. They allow multiple parties to work together on sensitive enterprise data in a controlled environment without giving collaborators direct access to each other’s raw data.

However, Clean Rooms should not be treated as a substitute for protecting sensitive data across its full lifecycle. Sensitive data may still exist in source systems, Delta tables, staging areas, Lakehouse pipelines, BI tools, AI workflows, notebooks, vector stores, exports, vendor feeds, temporary environments, and downstream copies.

The stronger security model is layered: use Databricks Clean Rooms for governed collaboration, and use runtime sensitive data protection to control which identities, applications, service accounts, pipelines, BI tools, and AI workflows can access sensitive values in cleartext.

Key Takeaways

• Databricks Clean Rooms and runtime sensitive data protection solve different problems and should be viewed as complementary controls.
• Clean Rooms govern specific collaboration and data sharing workflows.
• Clean Rooms do not eliminate sensitive data exposure across internal users, service accounts, pipelines, notebooks, BI tools, AI workflows, exports, vendor feeds, or downstream copies.
• AI adoption increases the number of places where sensitive data may be copied, transformed, indexed, cached, embedded, or consumed.
• Ubiq complements Databricks Clean Rooms by protecting sensitive values and enforcing identity-aware cleartext access at runtime.

Control Boundary View

What Databricks Clean Rooms Solve

Databricks Clean Rooms are designed to support controlled data collaboration. They allow organizations to work with customers, partners, or internal teams on sensitive enterprise data without giving collaborators direct access to each other’s raw datasets.

Databricks Clean Rooms use Delta Sharing and serverless compute to create a secure collaboration environment. Collaborators can share data assets such as tables, views, volumes, and notebooks into a central clean room environment. Approved notebook code can then run against the shared assets.

Common examples include:

• Partner analytics
• Advertising and audience collaboration
• Fraud analysis
• Customer or subscriber insight workflows
• Cross-organization data collaboration
• Joint analytics and AI workloads
• Approved internal and external data sharing

In this model, the Clean Room governs a specific collaboration workflow. Collaborators can contribute data assets and approved code, while the clean room controls how analysis is performed and how results are produced.

This is a valuable capability. It reduces risk within a specific collaboration pattern.

However, Clean Rooms are not designed to be a universal protection layer for every place sensitive data exists, moves, or is consumed.

Defining Runtime Sensitive Data Protection

In this page, runtime sensitive data protection refers to controls that determine whether a user, application, service account, pipeline, BI tool, notebook, AI workflow, or downstream system receives sensitive values in cleartext at the time of access.

This differs from collaboration controls.

A collaboration control governs how data is shared and analyzed within a defined workflow.

A runtime sensitive data protection control governs who can see sensitive values in cleartext.

These are related, but distinct, security objectives.

The Sensitive Data Lifecycle Is Broader Than the Clean Room

The central issue is scope.

A Databricks Clean Room may help control how approved collaborators analyze shared data. But sensitive data typically exists across a much broader lifecycle.

Sensitive data may be present in:

• Source systems
• Delta tables
• Unity Catalog-managed data assets
• Staging tables
• Transformed datasets and feature tables
• Data marts
• BI dashboards and extracts
• Databricks notebooks
• AI and RAG workflows
• Vector stores
• Model training and inference pipelines
• Temporary development and test environments
• Exports and file shares
• Vendor feeds
• Downstream applications
• Replicated datasets

Each of these creates a potential exposure path.

The fact that one collaboration workflow uses a Clean Room does not mean sensitive data is protected everywhere else it may be accessed, copied, transformed, exported, embedded, indexed, or consumed.

Where Exposure Still Exists

The key point is not that Clean Rooms are weak.

The key point is that Clean Rooms are scoped.

They help govern a specific collaboration workflow. They do not automatically govern every identity, application, notebook, pipeline, AI workflow, vendor feed, or downstream system that may access the same sensitive data.

Why AI, RAG, and Notebooks Make This More Important

Databricks is frequently used for analytics, data science, machine learning, AI, and data engineering workflows. That makes the sensitive data lifecycle broader than a single sharing or collaboration pattern.

Organizations are increasingly building:

• AI agents
• RAG systems
• Internal copilots
• Data science notebooks
• MCP-based tool integrations
• Vector databases
• Feature engineering pipelines
• Model training workflows
• Model inference workflows

These systems often require access to enterprise data. As a result, sensitive data may be copied, transformed, indexed, embedded, cached, or moved into new environments.

This creates additional risk:

• Sensitive data may be copied outside governed collaboration workflows.
• Temporary AI or development environments may contain cleartext data.
• Vector stores may preserve sensitive relationships, attributes, or identifiers.
• Service accounts and service principals used by AI systems may have broader access than intended.
• Shadow AI projects may emerge before security teams establish governance controls.
• Teams may bypass protection controls because they believe AI workflows require raw data.
• Notebook outputs, intermediate tables, or downstream tasks may expose sensitive values outside the original control boundary.

Clean Rooms do not solve these problems by themselves.

A Clean Room may govern a collaboration workflow, but it does not automatically govern every AI, RAG, notebook, agent, MCP, vectorization, model training, inference, or internal analytics workflow operating against the same data.

Why Runtime Sensitive Data Protection Still Matters

Clean Rooms answer the question:

How do we enable governed collaboration with approved parties?

Runtime sensitive data protection answers a different question:

Which identities and workflows should be able to see sensitive values in cleartext?

This distinction is important.

In a modern Databricks environment, sensitive data may be accessed by analysts, data engineers, data scientists, service accounts, service principals, pipelines, notebooks, BI tools, AI systems, and third-party integrations.

Not every identity or workflow that touches a table, view, feature, notebook, model workflow, or downstream dataset should automatically receive cleartext values.

Runtime sensitive data protection helps enforce least privilege by ensuring that sensitive values remain protected unless the requesting identity is authorized.

This can help:

• Reduce cleartext exposure
• Limit blast radius from compromised credentials
• Protect sensitive values across internal workflows
• Protect downstream copies and exports
• Support AI and analytics use cases without broadly exposing sensitive data
• Complement existing Databricks governance controls

How Ubiq Complements Databricks Clean Rooms

Ubiq does not replace Databricks Clean Rooms.

Clean Rooms remain useful for governed collaboration, approved analytics workflows, and controlled data sharing.

Ubiq complements Clean Rooms by protecting sensitive values before and beyond the collaboration workflow.

With Ubiq, sensitive fields can remain encrypted, tokenized, masked, or otherwise protected by default, while cleartext access is governed through identity-aware policy enforcement at runtime.

This allows organizations to:

• Protect sensitive values stored or processed in Databricks
• Control which users, applications, notebooks, pipelines, and service accounts receive cleartext access
• Reduce exposure across automation workflows and service principals
• Protect sensitive data used by BI tools, AI systems, RAG workflows, and notebooks
• Maintain protection when data is copied, exported, embedded, indexed, or consumed downstream
• Support least-privilege access at the field and record level

In this model:

• Databricks Clean Rooms govern collaboration.
• Ubiq governs which identities, applications, service accounts, pipelines, notebooks, BI tools, and AI workflows can access sensitive values in cleartext.

Together, they provide a more complete security architecture than either control can provide alone.

Internal Discussion Questions

When evaluating whether Clean Rooms are sufficient by themselves, teams should consider:

• Is every sensitive data workflow conducted through a Clean Room?
• Where does sensitive data exist before it enters any Clean Room workflow?
• Which users, roles, groups, service principals, pipelines, notebooks, and applications can access sensitive fields today?
• What happens when sensitive data is copied into staging tables, feature tables, notebook outputs, BI extracts, AI workflows, vector stores, vendor feeds, file shares, or downstream systems?
• How is sensitive data protected in temporary development, testing, troubleshooting, sandbox, or DevOps environments?
• Should a service account, service principal, analyst, engineer, data scientist, application, vendor process, or AI workflow be able to see cleartext simply because it has Databricks access?
• How would the organization reduce blast radius if a Databricks identity, service principal, token, API key, notebook, or downstream workflow is compromised?
• Which control ensures that sensitive values remain protected outside the Clean Room workflow?