Summary

If your Databricks environment includes PII, PHI, financial, or regulated data, Databricks alone is not enough. 

Databricks controls who can access data. 

Protegrity controls how the data itself is protected and reused. 

Enterprises use both because: 

• Databricks does not provide field-level data protection 
• Databricks does not persist protection when data is copied, shared, or reused 
• Databricks does not support enterprise tokenization or centralized re-identification 
• Databricks does not make regulated data safe for AI and GenAI workloads 

Where Databricks Security Stops

Databricks controls access to data, but it does not protect sensitive data itself. When regulated data is used for analytics, AI, or GenAI, Databricks provides no persistent tokenization, encryption, or AI data privacy controls to prevent exposure as data is reused and shared. 

Protegrity closes this gap by protecting sensitive data at the field level so it can be safely used across analytics and AI workflows. 

Where Databricks Security Stops

Enterprises typically add Protegrity when they: 

• Use PII, PHI, or financial data in Databricks 

• Train ML or GenAI models on regulated data 

• Share data across business units or regions 

• Need provable, persistent privacy controls for auditors 

How Protegrity and Databricks Work Together

Databricks responsibilities

Analytics and SQL processing

Machine learning and AI pipelines

Lakehouse compute and performance

Collaboration and workspace management

Protegrity responsibilities

Tokenization and encryption of sensitive fields

Persistent protection across copies and transformations

Centralized re-identification controls

Policy enforcement across analytics and AI workflows

Compliance controls that persist beyond Databricks

Where Protegrity Fits in Databricks

Protegrity operates as the enterprise data protection control layer for Databricks environments, applying field-level tokenization and encryption for privacy, compliance, and AI data security before analytics, machine learning, AI, and GenAI workloads run. 

Databricks handles computation. Protegrity controls exposure. 

Business Impact 

This is not about adding another tool. It’s about enabling Databricks to safely operate on regulated data. 

Databricks enables analytics and AI at scale. Protegrity enables the safe use of regulated data for AI at that scale. 

If your Databricks environment includes regulated data, Protegrity is not an add-on or an alternative — it is the data protection layer Databricks was never designed to provide. 

Protegrity vs Databricks — Capability Comparison

Category	Capability	Protegrity	Databricks
Focus / Architecture	Protects data in place (no vault)	Included	Not included
	Analytics lakehouse platform	Not included	Included
Data Protection	Field-level tokenization	Included	Not included
	Format-preserving encryption	Included	Not included
AI & GenAI	Safe ML / GenAI on sensitive data	Included	Not included
	AI and ML execution	Not included	Included
Governance	Central protection policy engine	Included	Not included
	Workspace / table access control	Not included	Included
Re-identification	Centralized re-identification control	Included	Not included
Compliance	Presistent GDPR / HIPAA / PCI controls	Included	Not included
Scalability	Cross-cloud policy consistency	Included	Not included
	Lakehouse compute scalability	Not included	Included
Integration	In-line protection in data pipelines	Included	Not included
Operations	Analytics performance & optimization	Not included	Included