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Troubleshooting Function Authorization Used by Production Row Filters and Column Masks

I recently encountered an issue where certain production row filters and column masks were not applying correctly in our Databricks environment. The root cause seemed to be related to function authorization—specifically, the production.security.email_mask function that was used to mask email addresses in sensitive data. I needed to understand why the function wasn’t being applied and how to resolve the authorization issue.

Understanding the Function and Its Role

The production.security.email_mask function is a critical part of our data governance strategy. It’s used in row filters and column masks to obscure email addresses in datasets without losing their structural integrity. When I started investigating, I realized that the function wasn’t being recognized by some users, which led to incomplete data masking.

To get a better understanding of the function’s definition and its permissions, I ran the first step in the curriculum: DESCRIBE FUNCTION EXTENDED production.security.email_mask;. This command provides detailed information about the function, including its return type, parameters, and any metadata that might be relevant to its usage.

DESCRIBE FUNCTION EXTENDED production.security.email_mask;

The output confirmed that the function was correctly defined and that it was intended to be used in column masks and row filters. However, the issue was likely related to permissions rather than the function’s definition.

Checking Function Permissions

Next, I wanted to verify who had access to the function. I ran SHOW GRANTS ON FUNCTION production.security.email_mask;. This command lists the users and roles that have been granted specific privileges on the function, such as EXECUTE or SELECT.

SHOW GRANTS ON FUNCTION production.security.email_mask;

The results showed that only a limited number of users had the EXECUTE privilege on the function. This was a key insight—it explained why the function wasn’t being applied in some contexts. I realized that the users or roles responsible for running the row filters and column masks didn’t have the necessary access to execute the function.

Verifying Routine Privileges Across the Catalog

To ensure I wasn’t missing any other relevant privileges, I ran the third step from the curriculum: SELECT * FROM system.information_schema.routine_privileges WHERE routine_catalog = 'production' AND routine_schema = 'security';. This query gives a more comprehensive view of all privileges granted on routines (functions and procedures) within the production catalog and security schema.

SELECT * FROM system.information_schema.routine_privileges WHERE routine_catalog = 'production' AND routine_schema = 'security';

The output confirmed that the email_mask function was the only routine in the security schema, and that the EXECUTE privilege was not granted to the roles or users that were expected to use it. This was a clear indication that the function’s authorization was misconfigured.

Resolving the Authorization Issue

With this information, I was able to take corrective action. I identified the roles and users that were responsible for applying the row filters and column masks and ensured that they had the EXECUTE privilege on the email_mask function. I also reviewed the access control policies to ensure that the function’s permissions were aligned with the data governance strategy.

After making these changes, I verified the function’s behavior by re-running the row filters and column masks. The email masking was now applied correctly, and the data was properly protected. The issue was resolved by understanding the function’s definition, checking its permissions, and ensuring that the right users had access to execute it.

This experience reinforced the importance of proper function authorization in data governance. Without the right permissions, even well-designed row filters and column masks can fail to protect sensitive data effectively.