Databricks vs Ubiq

Executive Summary

Databricks provides strong native security and governance controls for data stored, processed, and analyzed inside the Databricks platform. These include Unity Catalog, identity and access management, table permissions, row filters, column masks, attribute-based access control, service principals, audit logs, network controls, and customer-managed key options for supported encryption use cases.

These controls are valuable and should remain part of the architecture.

Ubiq addresses a different layer of the problem: protecting sensitive values themselves and governing whether users, applications, service principals, jobs, notebooks, pipelines, BI tools, and AI workflows can access those values in cleartext at runtime.

The strongest model is layered. Use Databricks-native controls for platform governance, workspace security, Unity Catalog access control, notebook and job governance, auditability, and lakehouse security. Use Ubiq for identity-aware runtime protection of sensitive fields and records across Databricks and downstream workflows.

Key Takeaways

• Databricks-native security and Ubiq runtime sensitive data protection solve different problems and should be viewed as complementary controls.
• Databricks is strong for governing access, permissions, catalogs, schemas, tables, notebooks, jobs, models, and platform activity inside Databricks.
• Ubiq protects selected sensitive values directly and controls whether an identity or workflow can access those values in cleartext at runtime.
• Databricks policies generally govern how data is accessed or displayed within Databricks. Ubiq helps protect sensitive values before, inside, and beyond Databricks.
• Ubiq is especially useful when organizations need to reduce cleartext exposure across service principals, jobs, pipelines, notebooks, BI tools, AI/RAG workflows, exports, and downstream systems.

Control Boundary View

Where Databricks Native Security Helps

Databricks provides important native controls for securing data, analytics, AI, and machine learning workflows inside the Databricks platform.

These controls help teams:

• Authenticate users and workloads
• Manage users, groups, and service principals
• Assign permissions to catalogs, schemas, tables, views, volumes, notebooks, jobs, and other platform resources
• Govern data access through Unity Catalog
• Apply row filters to restrict which rows a user can see
• Apply column masks to transform or redact sensitive column values at query time
• Use attribute-based access control and governed tags for scalable policy management
• Manage access to notebooks, jobs, clusters, SQL warehouses, and workflows
• Monitor platform activity through audit logs
• Secure data sharing and collaboration workflows
• Use customer-managed keys for supported encryption use cases
• Control network access and workspace-level security configuration

These capabilities are valuable for Databricks platform governance.

They help answer questions such as:

• Who can access this catalog, schema, table, view, volume, notebook, or job?
• Which users, groups, or service principals can query this dataset?
• Which rows should this user see?
• Which columns should be masked at query time?
• Which policies apply to this data asset?
• Which notebooks, jobs, or workflows accessed this data?
• How is data protected at rest inside supported Databricks environments?

For many workloads, these controls are necessary and effective.

However, they are primarily Databricks-platform controls. They govern access and presentation within Databricks-controlled paths.

Where Databricks Native Security Is Not Designed to Go

Databricks-native controls are not designed to provide persistent, identity-aware protection of sensitive values across every workflow where data may move or be consumed.

This distinction matters because sensitive data often does not stay inside one Databricks-controlled query, notebook, or job path.

Sensitive values may be accessed, copied, transformed, exported, joined, materialized, embedded, or consumed by:

• Internal users
• Data engineers
• Data scientists
• Service principals
• Jobs and workflows
• ETL and ELT pipelines
• SQL warehouses
• BI tools
• Data science notebooks
• Feature engineering pipelines
• Model training workflows
• Model inference workflows
• AI and RAG workflows
• MCP-based tools and agents
• API integrations
• Vendor feeds
• CSV, Excel, Parquet, or Delta exports
• Downstream databases
• Replicated datasets
• Temporary development or test environments

Databricks can govern access inside Databricks. But once sensitive values are returned in cleartext, copied downstream, exported, materialized, embedded into a vector store, or consumed by another system, native Databricks controls may no longer be the enforcement point.

That is the architectural gap Ubiq is designed to address.

Comparison Matrix

Key Architectural Differences

Platform Governance vs Sensitive Value Protection

Databricks-native controls govern access to Databricks resources and query or workflow results.

Ubiq protects selected sensitive values themselves.

This difference is important.

A user, notebook, job, or service principal may have legitimate access to a Databricks table, view, volume, feature table, or workflow, but should not necessarily see every sensitive value in cleartext. Ubiq allows organizations to protect sensitive fields and records directly, then decide at runtime whether a specific identity or workflow is authorized to receive cleartext.

Query-Time Policy vs Runtime Cleartext Authorization

Databricks row filters and column masks are powerful query-time controls. They help determine which rows are visible and how column values are shown when data is queried through Databricks-governed paths.

Ubiq adds a separate runtime cleartext authorization model.

With Ubiq, the question is not only:

Is this user, group, or service principal allowed to query this object?

The question becomes:

Is this identity, application, notebook, job, service principal, pipeline, BI tool, or AI workflow allowed to see this sensitive value in cleartext right now?

That distinction matters when sensitive values are accessed across many workflows, not just one governed table or notebook path.

Databricks Boundary vs Downstream Persistence

Databricks-native controls are strongest while data remains inside Databricks-controlled access paths.

But sensitive data often moves.

It may be exported to files, copied into downstream systems, materialized into derived tables, written into feature tables, joined into analytics datasets, consumed by BI tools, embedded into vector stores, or used by AI workflows.

Ubiq helps maintain protection of selected sensitive values beyond a single Databricks policy boundary. If protected values are copied, exported, embedded, indexed, or consumed downstream, they can remain protected unless an authorized runtime path reveals cleartext.

Role and Permission Governance vs Identity-Aware Field and Record Enforcement

Databricks uses users, groups, service principals, permissions, Unity Catalog policies, row filters, column masks, and workspace controls to determine access.

Ubiq can use identity-aware policy to govern access to sensitive values at the field and record level.

This makes it possible to apply more precise controls for:

• Different users querying the same table
• Different notebooks using the same dataset
• Different jobs processing the same data
• Different service principals with different cleartext needs
• Different BI users with different authorization levels
• AI workflows that should not receive raw sensitive values
• Downstream systems that should process protected values only

Native Platform Controls vs Cross-Platform Enforcement

Databricks-native security is designed for Databricks.

Ubiq is designed to protect sensitive values across broader enterprise data workflows, including applications, databases, warehouses, APIs, BI tools, pipelines, notebooks, and AI systems.

This matters when Databricks is one part of a larger data environment.

In many organizations, sensitive data exists across multiple platforms. Databricks may be the lakehouse, analytics, AI, and ML environment, but sensitive values may also appear in operational databases, data pipelines, event streams, APIs, analytics tools, AI workflows, vendor feeds, and downstream applications.

Ubiq provides a consistent sensitive value protection model across those paths.

When to Use Both

Databricks-native security and Ubiq are not mutually exclusive.

Organizations should continue using Databricks-native controls for:

• Authentication and authorization
• User, group, and service principal management
• Unity Catalog governance
• Catalog, schema, table, view, volume, notebook, and job permissions
• Row filters
• Column masks
• Attribute-based access control
• Governed tags
• Secure data sharing and collaboration workflows
• SQL warehouse and workspace governance
• Notebook and job access control
• Audit logging
• Network controls
• Native encryption and customer-managed key options where supported

Ubiq should be considered when organizations also need to:

• Protect sensitive values directly
• Reduce cleartext exposure inside Databricks
• Govern cleartext access by identity, role, application, dataset, and context
• Limit blast radius from compromised credentials, service principals, tokens, or overprivileged permissions
• Restrict cleartext access for service principals, jobs, pipelines, and automation
• Protect sensitive values used by BI, AI, RAG, notebooks, models, and downstream systems
• Maintain protection when data is copied, exported, embedded, indexed, or consumed outside Databricks
• Apply consistent field- and record-level protection across multiple platforms

The layered model is simple:

• Use Databricks for Databricks-native governance.
• Use Ubiq for runtime sensitive value protection.

How Ubiq Complements Databricks

Ubiq complements Databricks by protecting sensitive values before, inside, and beyond Databricks workflows.

With Ubiq, selected sensitive fields can remain encrypted, tokenized, masked, or otherwise protected by default. Cleartext access is granted only when the requesting identity or workflow is authorized by policy at runtime.

This allows organizations to:

• Store protected sensitive values in Databricks
• Query protected datasets while limiting who receives cleartext
• Control cleartext access for users, applications, service principals, jobs, notebooks, and pipelines
• Reduce exposure in BI and analytics workflows
• Protect sensitive data used by AI, RAG, notebook, model, and agent workflows
• Preserve protection when data is copied, exported, embedded, indexed, or consumed downstream
• Maintain separation between Databricks access and sensitive value authorization

In this model:

• Databricks governs platform access, lakehouse permissions, notebooks, jobs, AI/ML workflows, and auditing.
• Ubiq governs which identities and workflows can access selected sensitive values in cleartext.

Together, they provide a stronger security architecture than either approach provides alone.

Internal Evaluation Questions

When evaluating Databricks-native controls and Ubiq together, teams should ask:

• Which sensitive fields require protection beyond standard Databricks object access?
• Which users, groups, service principals, jobs, notebooks, and applications can access sensitive values today?
• Which workflows receive sensitive data in cleartext?
• What happens when sensitive data is exported, copied, joined, materialized, embedded, indexed, or replicated?
• Do BI tools, dashboards, extracts, and reports expose sensitive values outside Databricks?
• Do AI, RAG, notebook, MCP, vector store, model training, model inference, or agent workflows access sensitive values?
• Should service principals, jobs, or automation workflows receive cleartext, or only protected values?
• How would the organization reduce blast radius if Databricks credentials, service principals, tokens, or API keys are compromised?
• Which control determines whether a specific identity or workflow can see sensitive values in cleartext?
• Does sensitive value protection need to work across platforms beyond Databricks?

Summary

Databricks provides strong native security and governance controls for the Databricks platform. These controls are important for managing access, enforcing Unity Catalog policies, governing notebooks and jobs, auditing activity, and protecting supported data at rest.

Ubiq addresses a different layer: runtime sensitive data protection.

By protecting selected sensitive values directly and governing cleartext access through identity-aware policy, Ubiq helps organizations reduce exposure across Databricks users, service principals, jobs, notebooks, pipelines, BI tools, AI workflows, exports, and downstream systems.

Databricks controls access to the platform.

Ubiq controls exposure of sensitive values.

The strongest architecture uses both.