Data masking is a data access control and security measure that involves creating a fake but highly convincing version of secure data that can’t be reverse-engineered to reveal the original data points. It allows organizations to use functional data sets for demonstration, training, or testing, while protecting actual user data from breaches or leaks. Ultimately, data masking also helps ensure organizations remain compliant with a variety of data compliance laws and regulations, such as GDPR and HIPAA, while mitigating the risks of exposing sensitive data when used for critical business activities, including data analysis.
In this article, we’ll look at common data masking techniques, how to define a data masking standard for compliant analytics, what to look for in a data masking tool, and what some of the leading solutions are.
Data masking is an important practice for ensuring that only the right people have access to the right data and for the right purposes. This can be achieved using a variety of data masking techniques, including:
This technique combines data sets with similar attributes to make individual identification virtually impossible. Since the combined data could refer to any member of the data set, no individual can be identified. k-Anonymity is often compared to “hiding in a crowd.”
This technique scrambles data values into a non-readable form called ciphertext, and requires a specific decryption algorithm and key to unscramble it.
This technique involves injecting randomized “noise” into any data analysis environment in proportion to the level of threat to the data’s privacy. Therefore, differential privacy makes the original information accessible and available for analysis, but unauthorized viewers aren’t able to identify data subjects individually.
This technique replaces values from a data set based on a viewer’s authorization with a value “null,” while data redaction removes or substitutes all or part of a data value field based on user permissions.
This is the process of masking direct identifiers in a data set by replacing them with an artificial identifier called a “pseudonym.”
Examples of this technique, like data generalization, replace specific values from data sets with average values or broader ranges.
This technique swaps values in a data set for other realistic-looking values that don’t impact the data’s meaning or utility.
Data tokenization replaces secure data in a data set with a “token” that has no extrinsic meaning or value. A key that reveals the meaning of the token is separated from the data set by firewalls so that only users who are granted access to both are able to decipher and utilize the data.
With these techniques in mind, let’s look at how to define the right data masking standard for your business.
With so many data masking methods available, it’s important to examine the options and choose the best ones for your organization. A key driver in the decision-making process is understanding the inherent risk associated with your data and your organization’s overall risk tolerance. As such, conducting a risk assessment is an important step in helping you determine which standard is right for your business.
To do so, start by identifying the use cases that are relevant for your organization and the risks associated with each. For instance, if you need to share sensitive data with external researchers or analysts, you may identify the risk of in-transit data breaches or noncompliance with geography-based regulations. Keep in mind that reviews of historic internal and external data, as well as SME input, can be useful sources of risk identification. Next, analyze and evaluate each risk to help you understand the level of probable threat and what your organization’s tolerance level for that risk might be. Finally, you’ll want to look at how your organization treats risk in the context of data projects, including its use of preventive, directive, detective, and corrective controls.
Once a risk assessment is complete, you’ll be in a better position to determine the data masking standard and accompanying techniques that are right for your organization’s specific needs and risk tolerance.
[Read More]How to Design and Implement a Governance, Risk, and Compliance Framework for Data Analytics
As you look for a data masking solution, it’s important to find one that fits with your data masking standards. The best solutions will provide:
Data masking is an important facet of data access governance. It may seem simple in practice, but getting it right can be the difference between a straightforward path to properly secured data, and a complex, confusing experience that leaves your data exposed to breaches. Ultimately, any data masking solution you choose should offer all of the features listed above.
While there are numerous data masking solutions available on the market, Immuta, Privitar, and Redgate are rated among the best. Let’s look at each:
Immuta is the leading data security platform that delivers secure data access at scale. By automatically discovering, securing, and monitoring organizations’ data, Immuta ensures that users have access to the right data at the right time – so long as they have the rights. With Immuta, organizations can discover and classify sensitive data, enable all stakeholders – even non-technical ones – to author and enforce data policies, apply advanced privacy controls including data masking, and easily achieve provable compliance.
Privitar is a popular data masking and de-identification tool, though it is relatively limited in terms of its total capabilities. It allows collaboration across data owners, data consumers, and data guardians to safely and quickly deliver data compared to traditional methods.
Redgate is another leading data masking solution, providing a number of customization options and an interface many users describe as intuitive, though outdated. Redgate is often seen as a go-to solution for users seeking a simple approach with a limited feature set.
As with the process of defining data masking standards, choosing a solution that meets those standards is highly dependent upon your organization’s specific needs.
After completing a risk assessment to define your organization’s data masking standards and evaluating data masking solutions to meet those standards, operationalizing those standards might seem like the easy part – but it’s important to make sure what to expect to avoid any unanticipated roadblocks.
Implementing data masking techniques that sufficiently meet established standards comes down to which solution you choose. Those that separate policy from platform and enable dynamic policy enforcement across any cloud data platform equip teams with the agility, scalability, and protection they need to ensure their data masking standards are met, regardless of the compute environment.
The Immuta Data Security Platform does just this, providing data teams with attribute-based access control to dynamically enforce advanced data masking techniques at scale. With Immuta, organizations can ensure their data masking standards are met without bottlenecks or unnecessary overhead, and customers have peace of mind knowing their data’s privacy is never at risk.
To see for yourself how easy it is to implement data access control using Immuta, check out our walkthrough demo.
