What is data masking? Best practices, use cases and most common types
These days, data breaches and cybercrime have become critical issues for businesses around the world. In the past few years only, companies have lost millions to breaches of sensitive data that have endangered the privacy of their users and clients.
Just in 2021 alone, the average losses suffered due to data breaches have been over $4 million per breach. And while not all data exposed and lost in these incidents is equally valuable, PII (Personally Identifiable Information) is the most expensive of all the potentially compromised types of data.
With this in mind, it’s no wonder that data protection has become a number one issue for so many organizations. And while there are plenty of ways to protect data from malicious actors, data masking has turned out to be an invaluable technique for businesses looking to protect their PII data.
But what is data masking? How does it help companies and organizations protect themselves against cybercrime? We’ll go over the basics and examine these questions in more detail right here.
What is data masking?
Data masking is a method of creating realistic but, essentially, fake versions of your company’s organizational data. The aim of data masking is to protect the most sensitive data and PII, while also creating a functional alternative in situations where the real data isn’t actually necessary, such as sales demos, user training, or software testing.
As you might already know, data masking isn’t the only method of data protection. However, so far, traditional approaches such as passwords, firewalls, and commonly-used encryption methods have failed to provide data with sufficient protection. Today’s organizations and companies need to look past such traditional security measures.
In the world of modern cybercrime, counteracting hackers means employing a comprehensive, all-around data security solution. And while that kind of approach comes with a lot of moving parts, data masking remains a vital one.
Data masking techniques allow organizations to reduce the expenses resulting from data breaches, improve their level of compliance when it comes to data security, and proactively make their corporate data more secure.
Data masking can be complex, but its essence is always changing specific data values without altering the data format. The result is a version of the data that’s usable in certain situations, but without allowing for the genuine data to be reverse-engineered or deciphered if it gets into the wrong hands.
In terms of specific methods, there’s a variety of choices, such as encryption, character and word substitution, character shuffling, or a combination of all of them. The important thing is that all numerical values and characters will be changed beyond recognition and that this fake data will stay consistent across various databases—with unchanged usability in specific situations.
So, what types of data are protected through data masking?
There are various data types that can successfully be protected and replaced using masking. The most common are:
- Intellectual Property (IP)
- Payment Card Industry Data Security Standard information (PCI-DSS)
- Protected Health Information (PHI)
- Personally Identifiable Information (PII)
These data types can contain all kinds of valuable information, both for organizations and individuals. For instance, some of them can include trade secrets, patents, digitized medical records, info on mergers and acquisitions, but also customer details, student records, and employee records.
We’ll explain each of the data types above in more detail below.
Personally Identifiable Information
This type of data is masked to protect user privacy because every piece of it can be cross-referenced with other databases to reveal the identity of certain individuals. The information included in these data sets is usually passport numbers, full names, social security numbers, and driver’s license numbers.
Protected Health Information
Next up, we’ve got data gathered by various healthcare service providers, usually for the purpose of identifying and providing proper care. However, in the wrong hands, demographic information, insurance information, medical histories, and laboratory test results can all be disastrous at worst and uncomfortable at best.
Payment Card Information
Considering the extremely private nature of financial transactions, the PCI-DSS sets rules for the security of cardholder data, which merchants have to respect and follow. Among other things, that can include data masking practices to ensure user privacy.
This type of data is masked to protect valuable inventions, designs, business plans, specifications, and all kinds of intellectual property that can be valuable to individuals and organizations. Unfortunately, this is also the type of stuff malicious hackers and unauthorized users like stealing the most, which makes data masking even more important.
Why is data masking important?
Today, no data governance strategy is complete without data masking.
A majority of organizations have, at the very least, stopped copying production data for testing and development willy-nilly—using multiple, altered copies has become the norm. And disregarding this new, more secure approach to data management isn’t just a risky oversight—it’s even explicitly penalized and prohibited by industry regulations such as HIPAA7 and SOX6.
Across all industries, data governance experts agree on the importance of data masking in this new age of enterprise data security. There’s no way for organizations to ensure data security other than to eliminate sensitive data from development and testing environments. And the best way to do that without diminishing the effectiveness of those environments is to use masked data.
Traditionally, the security measures used to protect sensitive data hindered testing and development. After all, developers and testers need realistic, accurate data to do their jobs successfully. But, on the other hand, the use of real-world data led to all kinds of data and privacy violations.
So, how do developers and testers get the accuracy they need without compromising their organizations’ data security? Yep, you’ve guessed it—with data masking.
And this method is nothing less than a paradigm shift in the handling of sensitive data, especially when compared to various homegrown data security methods. Masked data is still useful because it retains the realism, integrity, and statistical properties of the original information, allowing for efficient and accurate development, testing and research. And yet, there’s no risk of sensitive data being disclosed to the public or malicious actors.
On the other hand, the resource-intensive and time-consuming nature of other data protection techniques can lead to even costlier issues with accuracy and repeatability.
So, to surmise —data masking is vital for companies in a few distinct ways:
- It helps organizations with General Data Protection Regulation (GDPR) compliance by significantly lowering the risk of sensitive data breaches, which is why data masking creates a competitive advantage for consumer-facing companies in any industry.
- It preserves the consistency and usability of data, while also making it practically useless to any malicious cyber attackers.
- It significantly reduces the risks that come with data sharing across cloud platforms, migrations to the cloud, and third-party app integrations.
- It removes the risks that come with project outsourcing, especially when the project uses sensitive data. Instead of relying on the shaky trustworthiness of third parties, data masking ensures no relevant data will be stolen or misused.
Data masking types
Depending on the specific use you have for data masking—and we’ll list those out in more detail below—you can choose from a variety of data masking types. The most common ones among them are on-the-fly and static data masking.
SDM — Static data masking
Generally, static data masking is done on a copy of production databases. That is the main use case for SDM. This method changes each data set so it seems precise enough for accurate training, testing, and development but without revealing any of the actual data. Here’s how the process usually goes step-by-step:
- A golden copy (master version) or a backup of your production database is transferred to another environment.
- While it’s in stasis, any unnecessary data for the subsequent processes is removed and this copy is masked.
- The masked copy is saved to a specific location.
DDM — Dynamic data masking
Dynamic data masking is called dynamic because it happens during the actual run time so that data is streamed directly from the production systems, and masked data isn’t saved in another location.
This masking method is usually reserved for role-based security processing for applications, for instance, the handling of sensitive medical records or customer inquiries with personal information. In those cases, dynamic data masking is used without the masked data being written back to the main production system.
DDM is frequently implemented through database proxies which change the queries that arrive at the original database and pass on the masked data to parties who have made the requests. There are no masked databases to prepare in advance with DDM, though there could be some performance hindrances to the application.
RevDeBug uses dynamic data masking to help its clients retain full control of what they record using the Browser Recording feature. Users can select the specific interaction areas that they want to record, for instance the fields that require the input of personal information, and mask the contents of some of them.
As a result, clients can gather personal data, such as card details, passwords and email addresses, without having to worry about privacy concerns.
Deterministic data masking
Basically, deterministic data masking means replacing column data with an identical value.
For instance, let’s say that your databases have multiple tables and there’s a “First Name” column in all of them. Depending on the processed information, there could be plenty of tables containing information on the same person, with the same first name.
With deterministic data masking, changing a “John” to a “Michael” shouldn’t just apply the change to the specific masked table but all of the other tables associated with the original John as well. And the masking should give you the identical result every time you run it.
This kind of masking happens when data is transferred from one environment to another, for instance from a production environment to development or testing. That’s why on-the-fly masking is a great choice for companies that have complex data integrations and continuously deploy new versions of their software.
With other methods, keeping updated backup copies of initially masked data would be difficult in this situation because you have to do it continuously. That’s why on-the-fly masking is used to only mask a specific subset of sent data when necessary.
Statistical data masking
In many cases, production data also contains important statistical information which needs to be protected through data obscuration methods. One of these techniques is differential privacy, which allows you to share information about specific patterns in a particular data set, but without sharing information regarding the actual individuals described by the data set.
What are some of data masking use cases?
There are plenty of use cases for data masking, and while we’ve mentioned some of them already, we’ll go over all the most common ones right here.
Data breach prevention
This is the big one, where data masking is used as a data security tool that provides security to sensitive data by anonymizing it. And if an organization is breached and the leaked data was masked properly beforehand, it won’t be possible to identify any individuals or entities the data describes.
Development and QA teams often can’t perform functional testing without the right data sets, which often contain actual user data. Masking retains the necessary testing integrity without revealing any actual user data.
Protection in transit
Masked data, once the masking process is completed, stays masked when it’s transferred somewhere or shared in the cloud. And if the data is breached in any way, no individuals or organizations can be identified, nor can their personal information be misused.
Personally identifiable information isn’t just protected by companies’ goodwill and common sense—all user data processed and gathered by entities online is protected by data privacy regulations. From HIPAA and GDPR to PIPEDA and GLBA, all of these regulations are supposed to maintain the confidentiality, security and wider anonymity of PII.
Data masking is an incredibly valuable implementation in the development lifecycle of any software product or platform. It helps with HIPAA compliance by removing key PII identifiers from data sets before they’re shared within any organization. Also, data masking helps with the Privacy By Design requirements set by GDPR Article 25.
Data masking: best practices
From all of the above, it’s easy to conclude that data masking is an essential part of the modern workflow in a digital-based, cloud-first business era. And when you’re ready to mask your sensitive data, here are a couple of valuable best practices to keep in mind:
Before you start masking any data, it’s important to see what data sets to focus on. As a result, you should catalog and identify:
- the most sensitive data points;
- the people who are authorized to view them;
- and their intended usage.
Even in the most security-conscious company, it’s safe to say that not all data elements are in need of masking. So, you shouldn’t waste resources trying to mask data points that are not at risk.
Instead, identify all of your sensitive data in non-production and production environments. Naturally, depending on your organization structure and whether the data is complex, this could take more or less time.
Define your data masking stack
For most large organizations, it’s not really practical to solely rely on one data masking tool—the data and its usage across your whole enterprise may vary wildly. Also, the techniques you choose to employ will also depend on your specific budgetary requirements and internal security policies.
So, before you pick your preferred set of techniques, make sure you’ve thought everything through and taken all the major factors into consideration. And make sure the same types of data are masked with the same techniques across your enterprise to preserve the ease of reference.
Secure masking techniques
The masking techniques you use and their accompanying data are just as critical to your security as the data you’re actually masking. For instance, deterministic masking may be done with a substitution technique, and if your lookup file that’s used for the substitution is breached by malicious actors, they won’t have a hard time revealing your sensitive data set.
So, organizations should ensure that only authorized personnel have access to your masking algorithms and related files.
Over time, it’s perfectly normal for organizational changes or specific project changes to lead to actual changes to sensitive data. And when that inevitably happens, you don’t want to start masking from square one every single time.
Rather, you should design the process to be quick, repeatable, and preferably automatic from the start. That way, you’ll be able to update masked data along with the original sensitive data without any issue.
Final thoughts on data masking
As the practice of data masking becomes crucial for businesses globally due to the ever-increasing threat of cybercrime, more and more companies are looking to implement some form of it to protect their users.
Here at RevDeBug, we have always considered the protection of user data to be of paramount importance. Even though some of our clients come from fintech and medical industries, where data protection is a must, all our partners can benefit from dynamic data masking to securely and effectively record the data input by their users.
When using our product, you can be sure that the sensitive user data you’re entrusted with is in good hands.
If you’d like to see how we apply data masking in practice, head to our free and interactive Live Demo. And, if you have any questions or comments, feel free to get in touch – we’d be delighted to hear from you!