Row-level security (RLS) in Snowflake is your secret to getting data control down to the most granular level. It enables you to decide which rows users can see based on their identity, role, or other attributes. This level of control is key for organizations to improve security around sensitive data.
Read on to learn exactly how to implement and optimize RLS for your Snowflake environment.
What Exactly Is Snowflake Row-Level Security?
Snowflake row-level security (RLS) is the process of restricting user access to specific rows in databases based on user roles. This lets you restrict access to sensitive information, allowing users to only access information relevant to their role or permission.
In other words: row-level security means, depending on their level of security, users will see different subsets of data in the same table.
How exactly does this work?
Remember: relational databases store data in a tabular format in rows and tables. Rows represent a specific value. Columns represent an individual attribute.
That means when a user runs a query, the database will dynamically apply the row-based security logic and only present the user with the data they’re authorized to access.
It’s important to note that row-level security is not a comprehensive data access control system when used only on its own. It should be used in parallel with other Snowflake security measures.
Row-Level Security vs. Column-Level Security
As you might have already guessed, row-level and column-level security operate in similar ways. Both apply data security rules at the database level each time a query is run. Column-level security only applies those restrictions at the column level instead of at the row level.
The key difference between the two lies in their scope:
- Column-level security hides entire data fields from unauthorized users
- Row-level security determines what records users can and can’t access
In other words: RLS gets you that granular control. You can filter data based on user context. That’s why many organizations prefer it.
Why Use Row-Level Security
Beyond being more precise, RLS comes with a variety of benefits.
Centralized access control
Row-level security is implemented at the database level. That makes it much more reliable than app-level access controls that can only manage data at the application level. You don’t have to worry as much about errors since security rules are applied consistently, no matter the application tier used.
Better data security
Strong RLS policies mean your database administrators can create granular access rules down to the most basic unit of data. Think about it: you can restrict users down to the per-role or per-user basis. That means a massive drop in unauthorized data exposure.
For example, a sales rep in California can only query customer records from their territory, even when running broader analytics queries.
Simplified data architecture
Row-level security also lets you simplify your organization’s data architecture. You can store data of differing security requirements in the same database or table.
Rule flexibility
RLS is highly flexible. You can always change your security policy to adjust to new user roles or add other highly customizable restrictions.
Administrators can continue to make adjustments based on:
- User role
- Physical location
- Type of data access
- User department
Simplified compliance
Since RLS means you can limit data access to a need-to-know basis for all users, it becomes key in compliance with privacy regulation policies like GDPR and HIPAA. And since it’s fully automated, it’s much easier to demonstrate compliance.
Row-Level Security Use Cases
No matter your organization or industry, you’ll always have some degree of sensitive data you need to keep secure. Your database administrators can use RLS so only authorized users can access, review, and edit sensitive data.
Here are a few common ways RLS is applied across different industries:
Cybersecurity
Security teams use RLS so analysts can only access threat data relevant to their specific infrastructure segments. Take a network security analyst for example. They should only be able to access firewall logs from their designated data centers, but they should also have shared access to global threat intelligence.
Gaming
Gaming companies use RLS to segregate player data by geographic region for GDPR compliance. That means that European player data is only accessible for other EU-authorized individuals, while maintaining all global analytics abilities.
Healthcare
Healthcare organizations face strict HIPAA compliance requirements when storing patient data in the cloud. RLS means healthcare workers can only access information for patients under their care. A cardiologist would only be able to see cardiac patient records, while someone working in billing can only see demographic and insurance information.
FinTech
Financial institutions run up against similar policies restraints to healthcare. Institutions with offices in multiple regions must handle GDPR.
For example, a bank would use RLS to localize data access, only allowing employees access to information relevant to their specific region.
Or take a multinational investment firm. It can implement RLS for traders, compliance officers, and executives, allowing traders to only access market data for their assigned regions, compliance officers to see audit trails for their jurisdictions, and executives full global visibility.
Ecommerce
E-commerce typically covers a much larger global footprint. Like FinTech organizations, restrictions based on geographic location are common RLS rules. Row-level security can also enable you to filter based on department, so employees can only view data relevant to their specific store or job description.
How to Implement Row-Level Security
Implementing row-level security is actually much easier than it sounds. There are two methods you can use:
- Secure views
- Row access policies
Implementing secure views
Secure views let you filter rows based on user identity.
Here’s how you can write this:
CREATE OR REPLACE SECURE VIEW sales_view AS
SELECT * FROM sales
WHERE region = CURRENT_USER();A FinOps team might use secure views to show cost data filtered by department. Here’s what that would look like:
CREATE OR REPLACE SECURE VIEW dept_costs AS
SELECT cost_date, service_name, cost_amount
FROM cloud_costs
WHERE department = CURRENT_USER_DEPARTMENT();This method does have one big drawback: if a user has direct access to the table, they can still see all of the data. That’s why best practice is to use Row Access Policies instead.
Implementing Row Access Policies
Row access policies let you enforce security at the table level. That means even direct queries will respect access restrictions.
Here’s a practical example for a FinOps team tracking cloud costs:
CREATE OR REPLACE ROW ACCESS POLICY cost_center_policy AS (cost_center VARCHAR) RETURNS BOOLEAN ->
CASE
WHEN CURRENT_ROLE() = 'FINANCE_ADMIN' THEN TRUE
WHEN CURRENT_ROLE() = 'DEPT_MANAGER' THEN cost_center = CURRENT_USER_DEPT()
ELSE cost_center = CURRENT_USER_COST_CENTER()
END;
ALTER TABLE cloud_costs ADD ROW ACCESS POLICY cost_center_policy ON (business_unit);This policy means department managers can only see costs for their units. On the other hand, financial administrations can maintain full visibility.
Row-Level Security Best Practices
The best way to stay on top of your Snowflake row-level security? These are our three not-so-secret secret techniques:
- Performance impact: Always remember that complex RLS policies slow query performance. Monitor execution times and consider indexing columns used in access policies when possible.
- Policy testing: Always, always, always test your RLS policies with sample users before pushing things live.
- Monitoring access patterns: Performing regular audits means you know when your RLS policies are no longer effective. Consider investing in a third-party tool to automate tracking unusual patterns or policies violations.
Snowflake row-level security is only one part of improving the security for your tech stack. Comprehensive Snowflake security means continuously monitoring user activity, query patterns, and access anomalies, and keeping on top of all that manually is more than just a full time job – which is why many companies are now looking to automate.
Tools like Yuki provide automated security alerting and usage pattern analytics that complement RLS policies. You don’t have to worry about identifying unusual access patterns or potential security violations, because Yuki will do that for you – no additional dev help needed.
Beyond security monitoring, Yuki optimizes your Snowflake platform performance and costs. Previous clients have seen savings up to 30% while strengthening their security.
Curious to see how much you can save? Reach out now for your free demo.
