Data masking defined
Data masking is a technique that protects sensitive information by replacing it with altered or fictitious data. In non-technical terms, it’s like putting a filter over the original data to make it usable for software testing, analytics, or training – but without exposing personal or confidential details. For example, if a company wants to share customer information with a development team in order to design and test new app functionality, they might mask names, credit card numbers, Social Security Numbers, or other Personally Identifiable Information (PII) to ensure that the data remains realistic, but still complies with data privacy regulations.
PII masking is a crucial component of maintaining privacy and compliance with regulations like GDPR, CPRA, and HIPAA. Whether data masking is persistent (data is altered in the dataset itself) or dynamic (data changes on the fly when accessed), data masking is a great way to strike the right balance between security and functionality in handling sensitive data.
How does Snowflake data masking work?
Snowflake data masking protects sensitive information by controlling who gets to see the actual data and who gets to see masked or anonymized versions of that data. Snowflake applies data masking policies at the column level in tables and evaluates user roles in real-time to determine the correct level of access. For instance, an employee in a finance role might see full credit card numbers, while someone in a general support role only sees partially masked numbers or placeholders.
What makes this approach effective for some applications is that the underlying data remains untouched, with the masking happening dynamically at query time. Administrators define these policies at the schema level, making it easy to implement consistent security measures across an organization. In many instances, this feature not only simplifies compliance with privacy regulations but also supports effective data governance.
Snowflake data masking features and benefits
Key features and benefits of Snowflake data masking include:
|
Feature |
Description |
Benefit |
|
Real-time data masking |
Applies masking policies dynamically at query time, without altering the underlying data |
Assures sensitive data is protected during access, while maintaining data integrity |
|
Column-level security |
Masks specific columns in tables on demand |
Provides granular control over sensitive data exposure |
|
Role-based access control |
Determines data visibility based on user roles and access privileges |
Ensures that only authorized users can view sensitive information, in compliance with data privacy regulations |
|
Flexible masking policies |
Supports various masking methods, including redaction, random data substitution, shuffling, and encryption |
Enables customization of data masking strategies to meet specific organizational needs and compliance requirements |
|
Centralized policy management |
Manages masking policies as schema-level objects, allowing centralized or decentralized administration |
Simplifies the enforcement of data governance policies across the organization |
|
Non-intrusive implementation |
Requires no changes to existing applications or data structures |
Facilitates seamless integration into current workflows without disrupting operations |
|
Support for data sharing |
Permits data to be shared with external parties while ensuring sensitive information is masked |
Secures collaboration and data sharing with partners, customers, or third-party vendors |
|
Scalability |
Scales to multiple columns across databases and schemas |
Allows data masking practices to grow alongside the organization's data assets |
|
Audit and monitoring |
Integrates with Snowflake's auditing and monitoring tools to track access and modifications to masked data |
Delivers visibility into data access patterns and potential security threats |
Snowflake data masking challenges
Snowflake's native data masking is robust but has its challenges, such as:
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Data discovery not included
Before implementing masking, sensitive data must be identified, yet Snowflake does not offer comprehensive, built-in data discovery and classification tools.
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Persistent masking not available
Another major limitation is Snowflake’s reliance on dynamic data masking, which only controls access in real time but does not physically alter sensitive data. While dynamic masking can be effective for immediate access control, it falls short when anonymized data is needed for testing or development environments. In such cases, persistent masking is critical to secure data during replication or export.
-
Advanced techniques not offered
Additionally, Snowflake does not offer advanced data masking techniques (beyond basic redaction and string manipulation), which may be required to address specific data types or compliance requirements.
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Sole reliance on RBAC
Another challenge is Snowflake’s dependence on role-based access control (RBAC) for policy enforcement. While suitable for predefined roles, RBAC can become burdensome in dynamic environments with overlapping roles.
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Limited data management capabilities
Furthermore, enterprises often source data from multiple, disparate, and legacy (e.g., mainframe) systems, with different data formats, schemas, and technologies. Snowflake can’t mask such data.
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Singular focus on column-level data
What’s more, Snowflake’s masking policies are column-based, lacking row-level logic. For instance, while masking all but the last four digits of a Social Security number is straightforward, selectively masking data for specific rows (e.g., VIP customers) requires logic that is beyond Snowflake’s native capabilities.
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Unexpected overheads
Moreover, setting up and managing these policies manually at the schema level can introduce significant operational overhead in large-scale, multi-database deployments.
In summary, Snowflake data masking primarily addresses data at query time, leaving gaps in securing data as it moves through different pipeline stages and environments (e.g., development, testing, and analytics). Although Snowflake supports external masking, it lacks built-in tools for persistent data protection and other types of data masking.
Snowflake data masking shines with K2view
Snowflake data masking is significantly improved by K2view data masking tools, particularly when it comes to handling diverse data sources and maintaining data integrity.
Unlike Snowflake, K2view can discover and mask sensitive data across multiple data sources – including Snowflake and external databases – while preserving referential integrity. K2view uses a business entity approach, which ensures that all sensitive data related to a given entity (customer, order, loan, etc.) is masked consistently across all systems.
K2view data masking technology eliminates the need for individual licenses per data source, as Snowflake requires. This centralized approach results in significant cost savings for businesses managing data privacy across different systems. Rather than purchasing separate licenses for each data source or platform, K2view offers a unified solution for data anonymization.
Finally, by unifying and masking data from multiple sources into a golden record, K2view ensures semantic consistency – allowing for more accurate and reliable data analytics, software testing, and data sharing. This holistic approach makes K2view an ideal choice for Snowflake customers looking to implement a more comprehensive and scalable data masking solution.
Learn how K2view data masking tools
close the gaps in Snowflake data masking.
