Masking Sensitive Data with Row-Level Security

The engineer stared at the query results. Clear text emails, phone numbers, account numbers. A security incident waiting to happen.

Masking sensitive data with row-level security is not optional. It is the fastest way to enforce access rules in the database itself, before data ever reaches the application. This prevents leaks, reduces complexity in API code, and keeps auditors satisfied.

Row-level security (RLS) lets you define policies that control which rows a user can see or modify. By combining RLS with data masking functions, you can return only masked values for sensitive columns unless the request meets strict conditions. For example, a support rep can view a customer’s masked email, while compliance officers with higher privileges see the full value.

A typical implementation involves:

1. Creating security policies on the table.
2. Using CASE statements or masking functions in views.
3. Granting role-based permissions that map to your RLS rules.

PostgreSQL supports native RLS, and you can combine it with views or computed columns for masking. SQL Server and Oracle offer built-in dynamic data masking features with RLS-like filtering through security policies. The patterns are the same: keep rules inside the database, enforce them with precision, and avoid leaking raw data at rest or in query results.

Best practices for masking sensitive data with row-level security:

• Identify all columns containing PII or other regulated fields.
• Define clear policies for each user role.
• Mask data by default, revealing it only when explicit conditions are met.
• Test RLS policies with realistic datasets and adversarial queries.
• Audit and log every access to masked columns.

When you mask sensitive data at the row level, you control both what data is available and to whom. This reduces your attack surface, lowers compliance risk, and centralizes sensitive data protection where it belongs — in the database layer.

Skip building custom masking logic in service code. Skip trusting client-side filters. Apply row-level security and masking directly in your datastore, and know that every access path respects the same rule set.

You can see this in action and deploy secure RLS with data masking in minutes. Try it now at hoop.dev.