Microsoft Entra and Snowflake Data Masking: Identity-Driven Data Access Control

Microsoft Entra and Snowflake Data Masking give you control without breaking your data pipelines. Entra manages identities and access policies. Snowflake Dynamic Data Masking hides sensitive fields at query time. Combined, they create a permission-driven layer on top of your warehouses.

With Microsoft Entra, you define granular groups and conditional access rules in a single identity plane. Those rules map directly into Snowflake roles via SCIM and SAML, so identity and access stay in sync. No manual updates. No stale permissions.

Snowflake Dynamic Data Masking uses masking policies attached to columns. A masking policy can return masked values for unauthorized users and clear values for roles you trust. You build once, attach to any number of columns, and Snowflake enforces it at runtime. This means you can store full customer data but expose only partial or anonymized fields to analysts, contractors, or testers.

The integration flow is simple:

1. Configure Snowflake as an enterprise application in Microsoft Entra.
2. Enable automatic user and group provisioning using SCIM.
3. Set up Snowflake roles to match Entra groups.
4. Write masking policies in Snowflake SQL, binding them to roles.
5. Test queries as different Entra user contexts to confirm policy behavior.

The result is a consistent, identity-driven system. Access changes in Entra propagate instantly to Snowflake. Masking is enforced by Snowflake’s query engine, with no changes to application code. Audit logs in both systems verify that sensitive fields are never exposed without authorization.

This approach scales. It works for PCI, HIPAA, GDPR, and internal policies. You avoid proliferation of database user accounts and keep your compliance posture tight.

See Microsoft Entra and Snowflake Data Masking working together now. Launch a live example on hoop.dev in minutes.