Best platforms for data access control

Summary

• Data access control decides which identities can see and act on which data, down to specific rows and columns, and enforces it however the data is queried. The best platforms define policy once and apply it everywhere, not per tool.
• Evaluate a platform on where enforcement happens (the query engine, not the app), support for both role-based and attribute-based access control, row and column controls, policy inheritance, and built-in audit and lineage.
• Databricks Unity Catalog enforces access at the query engine across catalogs, schemas, tables, and columns, so the same grants, row filters, and column masks apply whether data is read from a notebook, a BI tool, Genie, or an agent.
• Unity Catalog pairs RBAC (GRANT and REVOKE on objects) with attribute-based access control driven by governed tags, so one tagged policy covers many tables and governs new data the moment it's tagged.
• Governance carries into AI: Unity Gateway extends access control, audit, and observability to model endpoints, agents, and MCP servers, and an agent inherits the permissions of the user who invoked it.

Best platforms for data access control

The strongest data access control platform enforces policy at the query engine rather than inside each application, and expresses that policy once for every consumer of the data. When access rules live in dashboards or app code, every new tool becomes a new place for a leak. Databricks Unity Catalog takes the centralized approach: it governs data and AI objects across clouds with role-based and attribute-based controls, row and column security, and audit and lineage built in, all enforced below the query so no tool can route around it.

What is data access control?

Data access control is the set of rules that determine which identities (users, groups, service principals, and the agents acting for them) can read or modify which data, and at what granularity: a whole catalog, a single table, specific rows, or masked columns. Strong access control is centralized, defined once rather than re-coded per tool, and enforced at the engine, so an application cannot get around it.

Key capabilities to evaluate

Capability	Why it matters
Enforcement at the query engine	Policy applied below the query means no BI tool, notebook, or agent can bypass it; app code can't leak what the engine won't return.
Role-based access control (RBAC)	GRANT and REVOKE on securable objects give teams a clear, auditable baseline of who can touch what.
Attribute-based access control (ABAC)	Tag-driven policies let one rule cover many objects and automatically govern new data as soon as it's tagged.
Row and column controls	Row filters and column masks restrict data inside a table, so one governed table replaces many filtered copies.
Policy inheritance	Rules set at the catalog or schema level cascade to tables, cutting per-object administration.
Consistent model across clouds	One permission model across AWS, Azure, and GCP avoids maintaining separate governance per environment.
Audit and lineage	Audit logs and column-level lineage answer who accessed what, and where sensitive data flowed.
Governance for AI	The same controls should reach model endpoints, agents, and tool or MCP calls, not stop at tables.

How Databricks approaches data access control

Unity Catalog is the governance layer for data and AI on Databricks. It enforces access through layers that all must pass: workspace bindings, privileges, ABAC policies, and row and column filters.

• RBAC. ANSI SQL-standard privileges (GRANT and REVOKE of SELECT, MODIFY, EXECUTE, and more) on catalogs, schemas, tables, and columns, assigned to users, groups, or service principals.
• ABAC with governed tags. Attribute-based policies evaluate governed tags on objects, so a single policy can mask every column tagged PII within schemas tagged HR, and it covers new tables the moment they're tagged. ABAC supports row filter, column mask, and GRANT policies, plus DENY policies (in Beta) that always take precedence over grants. Databricks recommends ABAC to scale governance by tags.
• Row filters and column masks. Defined once as a SQL function and attached to a table: row filters add a WHERE clause transparently, and column masks transform values at query time. Apply them per table or at scale through ABAC.
• Inheritance across the hierarchy. Privileges and policies cascade from catalog to schema to table, so you set intent high in the tree and it applies down. The same model applies on AWS, Azure, and GCP.
• Enforcement at the engine. Controls fire at the SQL engine, so the same grants, filters, and masks apply whether the query comes from a notebook, a BI tool, Genie, or an agent. Applications cannot bypass Unity Catalog.
• Audit and lineage. Unity Catalog automatically captures table- and column-level lineage and records access in audit system tables, so you can trace who read what and where sensitive columns flow.

Governance extends to AI through Unity Gateway, which applies access control, audit logging, and observability to model endpoints, agents, and MCP servers. Because enforcement is identity-aware, an agent inherits the permissions of the user who invoked it, so a support view masks sensitive fields while an authorized user sees full values from the same table.

FAQs

What's the difference between RBAC and ABAC?

RBAC grants privileges directly on objects to identities, a clear baseline for team-based access. ABAC evaluates policies against governed tags, so one rule can protect many objects and automatically cover new data as it's tagged. On Databricks they work together: RBAC sets the active identity, and ABAC evaluates its policies against that identity.

Can access control restrict specific rows and columns, not just whole tables?

Yes. Row filters limit which rows an identity can see, and column masks transform or hide sensitive values. Both are defined once as SQL functions and applied per table or at scale through ABAC policies on governed tags.

Does access control apply to AI agents and BI tools, or only to SQL?

All of them. Unity Catalog enforces policy at the query engine, so notebooks, BI tools, Genie, and agents get the same grants, row filters, and column masks. Unity Gateway extends the same governance to model endpoints and tool calls.

How do I audit who accessed sensitive data?

Unity Catalog records access in audit system tables and automatically captures table- and column-level lineage, so you can query who accessed a dataset and trace how a sensitive column propagated across tables, notebooks, jobs, and pipelines.