Data masking replaces sensitive information with fake but convincing versions of the original data. Given the necessity for sensitive data protection, data masking must be adaptable to any data environment.
Regardless of the size, purpose, or tools in your data stack, there is a type of data masking that fits your use case. Each has its own strengths, but which is best suited for your needs?
To help you decide, here are some of the most common types of data masking:
Static data masking (SDM) masks data at rest rather than in use. It involves creating a copy of an existing data set and using masking techniques to scrub it of all sensitive information. This makes the data shareable without the risk of leaking sensitive information or giving access to unauthorized users.
Since SDM makes a copy of existing data, the masked output is detached from the initial data. Therefore, there is no connection between the original data set and the masked data.
Static data masking is best suited for software and application development or training environments. Generally, true data sets can’t be used without risking sensitive data leakage. But since static data masking scrubs real data sets of all sensitive information, it strikes the balance between utility and safety in a testing environment.
This is particularly useful for developers who are creating a new tool or application, since it allows them to test software with realistic data. They are able to run high quality tests without concern of inadvertently exposing data, resulting in more predictive outcomes. However, in an evolving data stack, data utility is often at odds with scalability.
Dynamic data masking (DDM) does not move or copy data. Instead, it takes a more agile approach, applying masking techniques as data moves throughout the testing/development/production environment.
DDM applies the same types of data masking techniques as SDM, albeit without copying data.
This maintains a single source of truth, avoiding confusion and data silos caused by creating many unnecessary copies of the data.
Dynamic data masking is the most widely applicable type of data masking. Since it is actively applied as data is streamed across the tech stack, it is not limited to the copy or storage location. Masking is enforced at query time, and therefore can determine (through dynamic data access control) what information should be masked for specific users.
Since dynamic masking does not require copying data and maintains a single source of truth, compliance is much easier to manage. Streamlined data policy enforcement automatically applies the necessary masking measures on any and all queries.
Deterministic data masking is a straightforward approach that simply replaces certain values with a different, predetermined value. For example, all appearances of the name “Marie” in a demographic data set could be set to change to the name “Maxine.”
The simplicity of this model is both its greatest asset and weakness. While easy to apply, the “code” is simple enough to potentially fall victim to reverse-engineering, making it a potential target for attackers and increasing the risk of a data leak.
Since values are masked consistently across data stacks, users can expect their data to be masked and protected whenever they require access. It is also celebrated for its simplicity, but as we’ve noted, this can also be a weakness.
Deterministic data masking is useful in limited cases where data is not at an increased risk of breach. Regardless of the level of risk, however, it will still remain easier to reverse-engineer than more dynamic masking. Although deterministic masking is simple and logical, it alone may not be the best choice for protecting your sensitive data.
Real-time data masking takes a very conservative approach to protecting data. Instead of masking entire data sets while accessing or copying them like DDM and SDM, this method masks data ad hoc, as it is requested by applications or users.
Real-time data masking is applied when data is sent between production and testing or development environments, and requires much less storage than methods like SDM since no data copies are required. This is inherently more secure than deterministic and static masking, as data is masked case-by-case to ensure protection and compliance.
Given its ad hoc nature, real-time data masking has a similar flexibility to dynamic data masking.
Since it is done “on-the-fly,” it does not rely on systems to be passively established and applied. This makes it practical for smaller data ecosystems with a variety of data use cases.
However, its ad hoc nature makes real-time masking inherently less scalable than a dynamic approach. Masking data on a case-by-case basis might work on a small scale, but as an organization grows its number of data sources and users, it becomes increasingly difficult and time-consuming to mask on-the-fly.
Each type of data masking has its own merits and specific use cases. However, there is only one type that has the scalability and flexibility to adapt to an organization’s growth and changes.
Dynamic data masking can support data masking techniques ranging from k-anonymization to differential privacy and beyond. These techniques, when automatically enforced at query time across the data ecosystem, ensure that sensitive data is always protected against leaks or breaches. All this is done while maintaining a single source of truth, and without negatively affecting time-to-access or efficiency.
The Immuta Data Security Platform provides you with a holistic, consistent, and scalable model that automatically applies dynamic data masking to protect sensitive data. With flexibility and agility, the platform de-risks your data so you can deliver new value from it.
To see how creating a policy in Immuta enforces data masking at scale, request a demo with our team today.
