As enterprises accelerate their adoption of AI and machine learning on Databricks, one challenge consistently emerges: governance without friction. How do you decide who can train, execute, or view sensitive AI models and datasets—while ensuring compliance and not slowing innovation?
Two complementary paradigms provide the answer: Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC).
RBAC offers simplicity and consistency. ABAC provides fine-grained, context-aware control. Understanding how each works, and how they can be combined, is critical to building a secure and scalable ML platform.
What is ABAC?
ABAC evaluates the attributes of the user, the resource, and the environment to determine access dynamically.
Attributes can include:
- User attributes — region, clearance level, project, department
- Model/data attributes — sensitivity, region, confidentiality tags
- Environment attributes — time of access, network location, device posture
Unlike static role assignment, ABAC is context-aware. Permissions are granted at runtime, based on rules that combine these attributes.
How ABAC Works in Databricks
In Databricks, ABAC is operationalized through Unity Catalog features:
- Row-level filters, column masking, and execution policies for datasets and models
- User attributes passed from identity providers such as Azure AD or Okta
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- Dynamic enforcement at query or execution time
Example: treating AI models as datasets
- Assets:
-
- gpt4_model tagged sensitivity=high
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- sales_forecaster_v2 tagged region=EU
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- customer_clickstream with columns tagged PII=true
- User attributes:
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- Alice: region=US, clearance=high
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- Bob: region=EU, clearance=medium
- Policies:
– – Restrict high-sensitivity models
CREATE MODEL POLICY high_sensitivity_access
AS (user.clearance = ‘high’);
– – Restrict model usage by region
CREATE MODEL POLICY region_based_access
AS (user.region = model.region);
– – Mask PII columns for non-cleared users
CREATE MASKING POLICY mask_pii AS (val STRING)
RETURN CASE WHEN user.clearance = ‘high’
THEN val
ELSE ‘REDACTED’
END;
- Result:
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- Alice can run gpt4_model (high clearance) but not sales_forecaster_v2 (wrong region).
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- Bob can run sales_forecaster_v2 (region match) but not gpt4_model.
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- Both can query customer_clickstream, but Bob only sees masked PII values.
Best fit: ABAC is ideal for environments where data sensitivity, residency requirements, or tenancy rules demand fine-grained, dynamic enforcement.
RBAC vs. ABAC — Side by Side
| Aspect | RBAC | ABAC |
| Basis | User role / group | Attributes of user & resource |
| Examples | Data_Scientists, AI_Researchers | region=EU, clearance=high, sensitivity=high |
| Granularity | Dataset / model level | Row / column level, execution-time |
| Policy type | Static | Dynamic |
| Maintenance | Simple, fewer changes | More complex, but highly scalable |
| Best for | Predictable org structures | Regulatory or dynamic environments |
Why You Need Both
RBAC and ABAC are not mutually exclusive — in fact, their power comes from working together.
- RBAC is best for coarse-grained control: onboarding new users by team or function, and assigning baseline privileges with minimal overhead.
- ABAC adds precision when regulatory compliance, residency rules, or PII handling require dynamic enforcement.
In mature AI environments on Databricks, this hybrid approach is the norm:
- RBAC = who you are
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- Alice → AI_Researchers group → EXECUTE on gpt4_model
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- Bob → Data_Scientists group → EXECUTE on sales_forecaster_v2
- ABAC = what attributes you have right now
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- Alice (region=US, clearance=high) → Access to high-clearance models only
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- Bob (region=EU, clearance=medium) → Access to EU models only, with PII masked
This layered governance ensures that access decisions are both predictable and adaptable.
The Bottom Line
As organizations scale AI development on Databricks, governance must scale with it.
- RBAC delivers simplicity and speed — foundational for team-based access.
- ABAC delivers precision and compliance — essential for protecting sensitive AI models and training data.
The true value comes from combining them: RBAC for organizational structure, ABAC for contextual sensitivity. This layered model keeps AI platforms both secure and agile, so enterprises can accelerate innovation while ensuring that compliance remains intact.
