How to apply row-level security in Redshift

Row-Level Security (RLS) is a crucial feature when it comes to controlling access to specific rows in a database table based on the identity of the user or other attributes. In Amazon Redshift, RLS isn’t natively supported like in some other databases (e.g., SQL Server or Oracle). However, with the help of views, user-defined functions, and access control mechanisms, you can implement effective row-level security in Redshift.

This blog will guide you step-by-step through how to apply RLS in Amazon Redshift to protect sensitive data and enforce granular access controls.


Why Use Row-Level Security?

RLS ensures users can only access the data relevant to them. For example:

A sales manager should see only their region's sales data.

An HR employee should only access records of employees in their department.

Without RLS, you’d need to create multiple copies of data or filter it on the application side — both prone to human error or performance issues.


Step 1: Identify Users and Their Permissions

First, determine how user access is controlled. This can be done by:

Mapping Redshift users or roles to regions/departments.

Creating a permission table that links user identifiers to the data they’re allowed to see.


sql


CREATE TABLE user_access (

    username VARCHAR,

    region VARCHAR

);

Populate this table with the access mapping:

sql


INSERT INTO user_access VALUES

('john_doe', 'East'),

('jane_smith', 'West');


Step 2: Create the Base Data Table

Assume you have a table with sensitive data:

sql

CREATE TABLE sales_data (

    sale_id INT,

    region VARCHAR,

    amount DECIMAL,

    sale_date DATE

);


Step 3: Create a Secure View

Now, create a view that filters data based on the currently logged-in Redshift user.

sql

CREATE OR REPLACE VIEW secure_sales_data AS

SELECT sd.*

FROM sales_data sd

JOIN user_access ua ON sd.region = ua.region

WHERE ua.username = current_user;

current_user is a Redshift function that returns the name of the logged-in user.


This view dynamically filters rows based on access mapping.


Step 4: Restrict Direct Access to the Base Table

To ensure users only access data through the secure view, revoke direct access to the base table:


sql


REVOKE SELECT ON sales_data FROM PUBLIC;

Then, grant access to the view only:


sql


GRANT SELECT ON secure_sales_data TO specific_user;


Step 5: Testing and Validation

Log in as different users and run:


sql

SELECT * FROM secure_sales_data;


You’ll see that each user only retrieves data for their permitted region. This confirms the RLS implementation is working.

Optional: Automate or Enhance

Use stored procedures or UDFs to simplify permission updates.

Use groups and roles to manage permissions for larger teams.

Implement auditing by tracking who accessed which rows (using logs or custom logic).


Conclusion

While Amazon Redshift doesn’t offer built-in row-level security, you can still achieve it with views, current_user, and permission mapping tables. This approach provides fine-grained access control without duplicating data or overcomplicating your architecture. With careful planning and good security hygiene, you can protect sensitive data and meet compliance requirements effectively in your Redshift data warehouse.

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