Protegrity and Snowflake
Secure Data Sharing for the AI Era
Snowflake enables data sharing. Protegrity ensures sensitive data stays protected.
Snowflake enables data sharing and analytics across teams and accounts. Protegrity adds persistent data protection—tokenization, encryption, and policy controls—so sensitive data can be safely shared and reused for AI without exposure.
Summary
If your Snowflake environment distributes data across business units, partners, regions, or marketplace consumers, sensitive data does not stay confined to a single controlled context.
Snowflake is designed to make data easy to share, replicate, and consume. Protegrity ensures sensitive fields remain protected wherever that data travels. Enterprises use both because:
- Snowflake access controls stop at the account or object level, not the data element level
- Shared or replicated datasets can be consumed downstream without protection
- Snowflake does not provide tokenization with centralized re-identification
- Snowflake does not provide tokenization with centralized re-identification
Where Snowflake Security Stops
Snowflake security is designed to manage access to tables, views, and shares. It does not protect sensitive data at the field level once that data is replicated, shared externally, or reused by downstream consumers.
When regulated data is shared for analytics or AI, Snowflake provides no persistent controls to prevent sensitive values from being exposed beyond the original context.
Protegrity closes this gap by protecting the data itself, so sensitive fields remain protected wherever Snowflake data is shared or reused.
When Protegrity Become a Requirement
Protegrity becomes essential in Snowflake environments when data moves beyond a single controlled account and becomes part of a broader sharing or collaboration model.
This typically occurs when:
- Data is distributed through Secure Data Sharing or the Snowflake Marketplace
- Multiple consumer accounts access shared datasets
- Business units operate independent Snowflake accounts across regions
- Shared datasets are used in Snowpark, Cortex, or AI-driven workflows
- Sensitive fields must remain protected even after replication or external consumption
How Protegrity And Snowflake Work Together
Snowflake
- Data storage and analytics
- Secure data sharing and collaboration
- SQL, Snowpark, and AI features
- Performance and scalability
Protegrity Responsibilities
- Field-level tokenization and encryption
- Persistent protection across shared and replicated data
- Centralized re-identification control
- AI data privacy and security enforcement
Where Protegrity Fits in Snowflake
Protegrity operates as the enterprise data protection layer for Snowflake, applying tokenization and encryption before data is shared, replicated, or used for analytics and AI.
Snowflake enables data collaboration. Protegrity controls sensitive data exposure.
Proven ROI & Business Impact
This is not about restricting data sharing. It’s about enabling Snowflake to safely operate on regulated data. Snowflake enables analytics and AI on shared data. Protegrity enables the safe use of regulated data for AI and collaboration.
If your Snowflake environment includes regulated data, Protegrity is the data protection layer Snowflake was never designed to provide.
Snowflake and protegrity – Capability Comparison
| Category | Capability | Snowflake | Protegrity |
|---|---|---|---|
| Core Model | Data warehouse & analytics platform | Included | Not included |
| Enterprise data protection layer | Not included | Included | |
| Data Sharing | Secure data sharing across accounts | Included | Not included |
| Persistent field-level protection after sharing | Not included | Included | |
| Replication & Distribution | Cross-region/account replication | Included | Not included |
| Protection that persists across replicas | Not included | Included | |
| Data Protection | Field-level tokenization | Not included | Included |
| Centralized re-identification control | Not included | Included | |
| AI & Advanced Analytics | Snowpark & AI feature execution | Included | Not included |
| AI data privacy for shared datasets | Not included | Included | |
| Governance | Role-based access & object permissions | Included | Not included |
| Centralized data protection policies across shares | Not included | Included | |
| Compliance | Platform-level compliance certifications | Included | Not included |
| Persistent protection for regulated data across consumers | Not included | Included |