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.
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.
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.
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.
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 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 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.
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.
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.
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