Data leaks often start with one unmasked field.

Mosh Databricks data masking stops that at the source. It gives you precise control over what data is visible, who sees it, and under what conditions. In seconds, you can protect sensitive fields like PII, financial records, or internal IDs without breaking pipelines or slowing analytic workloads.

Databricks integrates with a broad set of security tools, but masking at the query layer is where risk is cut. Mosh uses clear, rule-based policies to transform, redact, or hash specific columns in Delta tables. These rules apply across SQL, Python, and Spark jobs so masking is enforced whether data is queried, streamed, or batch processed.

Configuration is straightforward. Define masking policies once in Mosh. Assign them to tables or views in your Databricks workspace. The engine applies these policies at runtime, ensuring masked data never leaves the cluster unprotected. Because Mosh sits close to the compute, performance overhead stays low even with large datasets.

Security audits and compliance checks improve with this approach. Masked datasets can still be used for analytics, machine learning, and reporting while meeting regulations like GDPR and HIPAA. Teams avoid brittle workarounds like manually crafting sanitized copies, which often cause drift and errors.

Mosh Databricks data masking is built for scale. It supports role-based access controls, dynamic masking driven by user attributes, and fine-grained policy updates without downtime. Logs and metrics give visibility into every masking action, letting you prove compliance and detect attempted violations.

The risk of exposed data fields is permanent unless prevention is built in. Mosh delivers that prevention directly inside Databricks.

See Mosh running on Databricks with hoop.dev — launch a live demo in minutes and test data masking for yourself.