Column-level masking in Snowflake

How Column-Level Masking in Snowflake Works 

What It Is

Column-level masking in Snowflake helps us hide sensitive data from users who don’t need to see it. Instead of blocking entire tables, we can apply masking policies to specific columns—like email addresses or salaries—based on roles.

Why We Use It

• Controls access to specific columns, not entire datasets.
• Helps us meet privacy requirements without duplicating tables.
• Keeps sensitive data protected across environments like dev, QA, and prod.

How It Works

Here’s how Snowflake handles column masking:

• We define a masking policy using SQL.
• This policy acts like a filter—checking the user’s role and deciding what they can see.
• We attach this policy to one or more columns.
• When someone queries the column, Snowflake shows either the real value or a masked version.
  

Step-by-Step: Creating a Masking Policy

Let’s walk through a basic example where we want only HR users to see employee email addresses.

1. Create the Masking Policy

We define a policy using SQL. It checks the user’s role. If the role is HR_ROLE, show the real email. Otherwise, show a generic masked value.

2. Apply the Policy to a Column

We attach this policy to the email column in the employees table.

Now, any time someone queries that column, Snowflake checks their role and applies the policy.

Reusing the Policy Across Tables

Snowflake lets us reuse the same policy on similar columns in other tables. For example, we can apply the same email_mask_policy to an email column in a contractors table without writing new logic.

This helps us maintain consistency and reduce duplication.

Masking Numeric Data

We can also mask numeric values like salaries or bonuses.

For example:

• Finance roles see the actual number.
• Other roles see NULL or 0.

To do this, we follow the same steps:

• Create a policy that checks the user’s role.
• If not authorized, return a placeholder (like NULL).
• Apply the policy to the numeric column.

This is useful for sharing performance data while keeping compensation private.

Tracking Where Masking is Applied

To keep track of which columns have masking policies, we can use Snowflake's information_schema.columns view.

This allows us to:

• Audit which columns are protected.
• Check if policies are applied consistently.
• Identify columns missing masking rules.

Best Practices for Column Masking

• Use clear naming for policies (email_mask_policy, salary_mask_policy) to make them easier to manage.
• Keep logic simple : avoid overcomplicating the policy with too many conditions.
• Document the purpose of each policy in a central table or within your team’s governance process.
• Test across environments especially in dev or staging—to make sure masking works consistently.

Real-Life Example: Customer Support Access

Let’s say our support team needs to see basic customer info but not payment details or phone numbers.

• We attach a masking policy to credit_card_number and phone_number.
• The policy allows access only for roles like PAYMENTS_TEAM or SECURITY_TEAM.
• All other users just see "masked" or a generic value.

This setup protects sensitive data while still supporting team collaboration.

Common Limitations

While column masking is powerful, there are a few things to watch for:

• One input column only: Policies can only use the value of the column they’re attached to.
• Only SELECT queries are affected: Other operations like INSERT or COPY aren’t masked.
• Not meant for full security: Masking helps with access control but doesn’t encrypt or fully hide data from backend processes.

Summary

Column-level masking in Snowflake is a practical way to protect specific fields without blocking access to the entire dataset. It’s flexible, role-based, and easy to maintain at scale.

If we’re sharing data across teams or environments, column-level masking is a helpful tool for minimising risk. It keeps access focused, simple, and transparent—without slowing down collaboration.