Static vs dynamic data masking

What is data masking?

Data masking is a critical process used to protect sensitive data, such as personally identifiable information (PII), by creating sanitized versions that retain the essential characteristics necessary for business use without revealing any sensitive information. This technique allows for meeting data privacy and compliance requirements while still preserving the data’s utility; it is therefore particularly useful in development and testing environments, where real data cannot be used but realistic data is essential.

What is static data masking?

Static data masking (SDM) involves permanently modifying data at rest to create a new dataset that maintains the data’s usefulness for testing or development while eliminating all sensitive information. While this is a permanent modification of the data, it can be performed in such a way that the modifications are made to a copy of the original data, instead of modifying the original data itself. In the case of software development workflows, this means leaving the production data intact and only modifying a copy of the production data to generate a safe-to-use test dataset.

Pros of static data masking

• Data integrity: SDM optimizes test data integrity and usability by maintaining relationships and ensuring that referential integrity is consistent with that found in the source data, making the data ideal for testing and development environments.
• Compliance and security: By permanently altering data, SDM helps achieve compliance with regulations like HIPAA or GDPR, as the output no longer contains sensitive information, thereby eliminating the risk of data leaks from lower environments.
• No granular security needed: Because all sensitive data is replaced, there's no need for complex object-level security measures.
• Performance: Since transformations are applied before the data is made available, there's no performance penalty or runtime overhead during database operations, which means there’s no impact on query performance when using the masked data set.
• Read/write data: SDM allows for the creation of data that is read/write; in other words, it can be written to and modified, a crucial trait for data in test and development environments.
• Broad protection: It protects copies of production data in diverse scenarios, including access via applications and back-end native queries.

Cons of static data masking

• Batch processing: Static masking is performed as a batch process, rather than real-time, which can in some cases be time-consuming, particularly with large datasets.
• Storage requirements: Static masking requires additional storage space since it most often creates a copy of the original dataset. This requirement can be mitigated by subsetting performed in tandem with the masking operation, to minimize the masked dataset.

Static data masking in action

Static data masking is particularly useful in regulated industries such as healthcare, financial services, and insurance. It’s worth noting that with the expansion of data privacy laws, more industries are falling under the umbrella of needing to adhere to regulatory requirements to protect all consumer data.

By way of example, imagine a financial institution developing a new customer insights tool. Through static data masking, they can generate a fully functional, de-identified dataset of customer transactions, including realistic credit card numbers, for example, that developers can use to build and test their tool, ensuring compliance with financial regulations.

Or consider a scenario where a health tech company needs to develop a new diagnostic tool. Using SDM, they can create a dataset to de-identify protected health information (PHI) with realistic but altered patient records, ensuring developers have robust data to work with while fully complying with health data regulations.

What is dynamic data masking?

Dynamic data masking (DDM) modifies data in transit without altering the data at rest. It uses a database proxy to mask data based on user roles and query specifics, presenting masked data to the user while the original data remains unchanged. The data is not masked physically in the database; it is masked only in the query result.

Pros of dynamic data masking

• Near real-time masking: Data is masked on-the-fly as it's requested based on the query, which means there’s no upfront batch processing step.
• No additional storage requirements: Since the original data is not altered and no additional at rest data is created, no extra storage space is required.
• Selective masking: Only the data needed for the specific query is masked, rather than the full database.

Cons of dynamic data masking

• Read-only data: This is the crucial disadvantage for developers to know, since it means that dynamically masked data cannot be used in testing environments. Dynamically masked data is read-only and cannot be modified, which prevents its use in development environments where data manipulation is necessary. Additionally, dynamic masking doesn’t give developers access to local data sets for development, and stored procedures cannot be dynamically masked. 
• Complex configuration: Dynamic data masking requires meticulous setup to define access controls and masking rules, which become complex and challenging to maintain over time.
• Limited use cases: Since it is not suitable for creating development and test environments where developers need data to be editable, its use is limited to specific cases such as compliance checks or user queries in production environments.
• Single point of failure: The proxy through which the data is dynamically masked can introduce a bottleneck or data security risk if compromised.

Examples of dynamic data masking

Examples of dynamic data masking are confined to read-only use cases that require RBAC at the object level to limit data access based on custom permissions, such as in customer service inquiry workflows. As a concrete example, you could imagine a corporate scenario where different departments need to access the same customer database. DDM can ensure that HR sees only employee names, emails, and social security numbers, while the finance department also sees their salary details. Each query is tailored so that only the necessary information is visible to each department.

Static vs dynamic masking: use cases

Static data masking is ideal for environments requiring complete, protected datasets that can be edited, such as in software testing or development. Dynamic data masking, meanwhile, is suited for operational databases where only certain data needs to be masked in real-time based on a user’s request, such as in customer support or BI reporting.

Static vs dynamic masking: data protection

Static masking provides more comprehensive protection as it generates permanently altered data, eliminating the possibility of sensitive data exposure or leaks by providing the data end-users with irreversibly masked datasets. Dynamic masking, while flexible, relies on runtime configurations that could potentially be bypassed, allowing users to connect directly to the production database.

Benefits of static over dynamic masking

Static masking is notably beneficial in scenarios where data integrity, comprehensive data de-identification, and compliance are critical. It provides a stable, performance-efficient environment for development without the complexities and limitations of dynamic masking.

By the very nature of statically masked data being read/write and dynamically masked data being read-only, static masking is the appropriate and superior option for generating data for use in software testing and development. Static masking performed on copies of production data makes that data safe and usable for developers.

Data masking with Tonic.ai

Tonic Structural stands out as an industry-leading solution for static data masking tailored specifically for developers. It offers an intuitive UI, native data connectors, and sophisticated algorithms to ensure that your test data remains realistic and useful while achieving maximum privacy and compliance. 

By enabling the generation of high-fidelity test data that mimics the characteristics of production data, Structural empowers developers to confidently build and test their applications, accelerating development cycles while adhering to data privacy standards. Get started with a free trial of Structural today, or connect with our team to learn more.