Static Data Masking vs. Dynamic Data Masking; What’s the Difference?
Solutions Review editors curated this comparison of static data masking vs. dynamic data masking so you know the difference. The contents of this resource originally appeared in Five Data Masking Best Practices for Securing Sensitive Data by Immuta CTO Steve Touw.
Data masking – also known as data obfuscation – is a form of data access control that takes sensitive information in a data set and makes it unidentifiable, but still available for analytics. This enables organizations to effectively store, access, and derive value from the data, while preserving its safety and anonymity. This is key in today’s business environment, where all businesses regardless of size, location, or industry are potential targets for attackers, internal bad actors, and/or privacy regulators.
In addition to the business and safety benefits, data masking also helps from a customer standpoint. Due to growing privacy and governance requirements, today’s customers expect that organizations are taking the necessary steps to secure their sensitive data and use it responsibly. If this trust is broken by misuse or a data breach, it may irreparably damage consumer confidence and brand reputation. Masking techniques reduce the risk of breaches and internal bad actors using data irresponsibly, while meeting privacy regulations sweeping the globe. This ultimately protects consumers’ privacy and helps maintain a higher level of trust between an organization and its customers.
When it comes to implementing a data masking strategy, there are two primary types – Static Data Masking (SDM) and Dynamic Data Masking (DDM). Both have strengths and weaknesses, and thus one may be better suited for a specific data environment than the other. Companies should evaluate which approach best meets their needs by understanding the main differences between them:
Static Data Masking vs. Dynamic Data Masking
Static Data Masking (SDM)
At a high level, static data masking masks data at rest rather than in active use. This is accomplished by creating a copy of an existing data set and hiding or eliminating all sensitive and/or personally identifiable information (PII). This copied data is then free to be stored, shared, and used, free of any sensitive information, and is completely detached from the initial set.
This type of masking is a better fit for environments where data does not change over time and is only used for a single purpose, such as software and application development or training. A large downside to SDM, however, is that it is unable to easily scale when larger data sets and/or combinations of access levels are introduced. Additionally, since the data is static, it is not well suited for analytical use cases. For these reasons, organizations should stay away from SDM for analytical purposes.
Dynamic Data Masking (DDM)
Unlike SDM, DDM applies masking techniques at query-time, and does not involve moving, copying, or separating the data from its original source. This helps teams avoid any confusion and silos around data copies that have been scrubbed and masked for different reasons. It also remains updated and “live,” which is critical for analytics.
Since DDM is not tied to where the data is copied or stored, it is often considered to be the most widely-applicable type of masking. It also easily scales to more complex policy scenarios and use cases, making compliance much easier to manage.