Snowflake gives you two main options for controlling access to sensitive data: column-level masking policies and tag-based masking policies. Both are natively supported, but one clearly scales better. 

At ALTR, we help organizations take full advantage of Snowflake’s native tag-based masking policy capabilities—automating the tagging, simplifying policy management, and giving teams full visibility and control across their data environment. 

If you’re deciding between the two, here’s why we recommend tag-based masking policies in Snowflake as the smarter, more scalable choice—especially when managed through ALTR. 

What Are Column-Level Masking Policy vs. a Tag-Based Masking Policies in Snowflake? 

Before diving into why tag policies are more effective, it’s important to understand how each policy type works in Snowflake: 

• Snowflake Column-Level Masking Policy: This policy is applied directly to a specific column in a table. It controls how the data in that column is masked based on the user’s role. For example, a column containing Social Security Numbers (SSNs) might be fully visible to an admin but partially masked for a support analyst. 

• Snowflake Tag-Based Masking Policy: This policy is tied to a Snowflake tag—a metadata label like PII, Confidential or Sensitive. Once a tag is associated with one or more columns, the masking policy automatically applies to all tagged columns. As new columns are added and tagged, the policy applies without any additional configuration. 

While both approaches are supported in Snowflake, only one is truly designed to scale with your data platform. Here’s why ALTR consistently recommends tag-based masking policies for Snowflake users. 

Tag-Based Masking Policies Are More Scalable

As your Snowflake environment grows, so does the complexity of managing security. With Snowflake column-level masking policies, every sensitive column requires an individual policy. That means more manual work, more room for error and more maintenance as schemas evolve. 

With tag-based masking policies, you apply the masking logic once—at the tag level—and Snowflake automatically applies it to all columns with that tag. As new columns are added and tagged (e.g., with PII or Financial Data), the policy is enforced without any additional effort. 

This makes tag-based masking policies: 

• Easier to maintain across growing data environments 

• Less prone to gaps in coverage 

• A better fit for dynamic or agile data architectures 

Tag Policies Offer Greater Flexibility for Role-Based Data Access

In real-world use cases, different user roles often require different views of the same data. A customer support agent might need to see the last four digits of a credit card, while a data analyst sees only a masked string. 

With tag-based masking policies in Snowflake, it’s easier to apply role-based data masking across multiple columns and use cases. The same column can be masked differently depending on the querying user’s role or job function. 

By contrast, column-level masking policies require you to define that logic separately for each individual column, which increases policy sprawl and makes it harder to manage changes over time. 

With tag-based policies, you can: 

• Create masking policies that adapt to different user roles 

• Apply consistent logic across many columns using a single tag 

• Streamline your data access control strategy 

Tag-Based Policies Are Easier to Manage Across Teams and Data Sources

Column-level masking policies in Snowflake require data engineering or security teams to keep track of every sensitive column and ensure that the correct policy is applied. This is manageable in small environments but becomes a nightmare at scale. 

By contrast, managing your data protection strategy using Snowflake tags and tag-based masking policies simplifies administration: 

• Assign a tag like PII to every column with personally identifiable information. 

• Define a single tag-based masking policy once. 

• Ensure that the correct masking rules apply automatically—no matter how many tables or columns you add. 

For organizations with distributed teams, shared data environments or fast-changing schemas, this is a game-changer. It drastically reduces the manual burden and the risk of oversight. 

Column-Level Masking Policies Still Have a Place—But They Don’t Scale 

To be clear: Snowflake column-level masking policies still serve a purpose, especially in tightly controlled environments with static schemas or limited sensitive fields. If you only need to protect a handful of columns and don’t anticipate frequent schema changes, column-level policies might be sufficient. 

But in most enterprise or growth-stage environments—where new data sources are constantly being added and user roles vary widely—tag-based masking policies are the more strategic long-term solution. 

How ALTR Helps You Manage Masking Policies in Snowflake 

ALTR makes it easy to implement and manage tag-based masking policies across your Snowflake environment. Our platform offers: 

• Automated tagging of sensitive data, including PII and financial data 

• Centralized policy creation and management at the tag level 

• Monitoring, auditing and alerting for every data access event 

We help organizations of all sizes enforce strong data access controls, simplify compliance and reduce the operational complexity of securing cloud data. 

The Bottom Line 

If you’re still relying on Snowflake column-level masking policies to manage your data security, it’s time to consider a smarter approach. Tag-based masking policies are more scalable, more flexible and far easier to manage—especially when paired with ALTR’s automated platform. 

Want to see how tag-based policies can transform your Snowflake security strategy? 

Talk to us today and start securing your data the scalable way.