When I talk to CIOs about data security, I often stress that there are no perfectly secure solutions. Instead, our goal is to make the hacker’s cost of obtaining data greater than the value of that data. Data masking and data encryption represent two different approaches to increasing data security.
But data masking and data encryption each offer vastly differing values based on their use case. This blog will compare these two approaches and provide practical guidance on when and why they are most useful.
What Is Data Encryption?
In simple terms, data encryption transforms cleartext data with a key or a code into scrambled data. This scrambled data can be turned back into cleartext with the correct key or code.
The goal is to make it expensive or computationally impossible to brute force all possible permutations of the key or code. This makes it costly or challenging to recover the original data, making encrypted data less attractive to hackers.
And Data Masking?
Data masking, on the other hand, is a method of protecting sensitive data by replacing the original value with a fictitious, but realistic, equivalent. In this blog, we’ll focus specifically on static data masking when comparing masking vs. encryption.
Data Masking vs. Data Encryption: What’s the Difference?
The main difference between data masking and data encryption is that masking protects data by replacing it with fictitious data while encryption transforms data into unreadable data that only authorized users can read.
There are many differences to consider when weighing data masking vs. data encryption. For decision-makers, like CIOs, I recommend considering the following aspects.
Business Value
Most encrypted data has no analytical value until it is transformed back to unprotected cleartext.
Masked data, on the other hand, can be used as-is for testing. This preserves data formatting and typing. Masking also ensures referential integrity with the real data. Plus, masked data can also preserve aggregate statistics to support many types of statistical analyses.
So, masked data delivers greater business value than encrypted data.
Reversibility
Encryption is reversible with a key. Masked data is based on non-reversible algorithms.
So, masked data offers greater protection against reversibility than encrypted data.
Susceptibility
Encrypted data is susceptible to a brute force attack. Masked data based on strong methods with safeguards against simple data types and contextual attacks.
So, masked data is generally not susceptible to brute force attacks — unlike encrypted data.
Is Data Masking More Popular Than Encryption?
In our recent State of Data Compliance and Security Report, we found that 66% of organizations use static data masking to protect data — a higher rate than the 53% who use data encryption. Find out what else 250 global enterprises had to say about protecting sensitive data in non-production.
Data Masking vs. Encryption: When to Use What
All that said, decision-makers should not draw from this comparison that encryption plays second fiddle to masking. Selecting between encryption and masking is a question of use case. Encryption can be the right choice for a few use cases. Masking is the best choice for many others.
Nature vs. Nurture
We have all heard the argument of nature vs. nurture. It turns out that both the nature of your data and how you nurture it differ between production and non-production data.
Production data runs your business. It must be real. It must be protected. It is highly regulated. We spend a lot of time backing up production data (and often in great granularity) because losing production data means losing business.
But non-production data is different. Non-production data is generally used to enable the systems and software that make it possible to conduct business. While non-production data does need to be representative — and have the look and feel of realistic data — it does not need to be real to accomplish its purpose.
Fictitious data does not have any real customers to protect. Leaked fictitious data does not even meet regulatory reporting criteria such as GDPR.
Use Case: Production
Production systems must operate on, store, or transmit sensitive real data to accomplish your business purpose. Production data has high business value. It needs the most secure access controls and the most hardened protections.
Data encryption is the preferred option between the two because the intent is to fulfill an order to a real customer. Masked data, which is realistic but fake, does not make sense for that intent.
Use Case: Development and Testing
Development and testing systems need representative data that provides reasonably accurate test results. But this representative data needs to be easy to obtain or (re)generate so that companies can deliver robust software at velocity.
Test data’s business value is tied to its value for testing code. And that testing value is related to how it is:
Fresh and synchronized.
Adherent to referential integrity.
Readily available.
Thus, it is not surprising that security often takes a backseat to speed and expedience. Many times, the speed of software delivery is far more important to the business than protecting data. This attitude often creates a security concern among those same developers and testers, who have both elevated access and elevated intelligence about how your software works.
Hackers target development and testing environments because they are usually less protected than the “iron fortress” of production. Further, development and testing commonly involve hundreds of individuals, whereas production is just a handful. And it is still all too common that production data leaks into these environments because:
People simply copy it over.
Production data is more representative and gives better testing results.
It’s too hard/costly to (re)generate test data for each test run.
Encryption does not help much here. Developers and testers are already on the inside. Either they have access to the dataset, or restricting their access becomes a complex impediment that brings software delivery to a crawl.
Alternatively, it is irrelevant if developers and testers have access to masked data because it is not real. The focus shifts back from security to velocity, where it matters most.
Use Case: AI, BI, Analytics, and Quantitative
Quants typically need data that meets data privacy standards. They accomplish this through things like data scrambling, data masking, noise addition, substitution, redaction, or generalization.
But they also need to maintain the aggregate properties of the dataset to make it usable for statistical analysis. And the intent is to analyze that data to arrive at actionable insight. This data has high business value. And, depending on how the data is transformed, it may still need secure access controls and hardened protections.
Although data encryption is an option here, the cost in terms of performance and usability in this use case often makes it impractical. And some forms of encryption that allow analysis of aggregate properties have unique vulnerabilities.
Masked data presents data that is realistic but fictitious. So, it can maintain aggregate properties for statistical analysis, making it the preferred choice.
Enterprise-scale masking platforms like Delphix offer the capacity to mask data at speed, scale, and on a regular schedule. That provides a clear economic advantage on top of the security benefits. Discover more >> What Is Delphix?
Why Is Data Masking the Best Choice Over Data Encryption?
Production data needs to be real. But the truth is that there is a huge volume of data that does not live in production. In the State of Data Compliance and Security Report, 75% of organizations reported an increase in the volume of sensitive data in non-production environments.
Non-production data is different. The use cases and business value for that data that lives in non-production are much different. And because they are, masked data is the best choice for those use cases.
Fast Data Matters
For the development and testing use cases, it is not whether the data is real. Rather, it’s whether the data is representative, fresh, synced; whether it maintains referential integrity; and whether it’s readily available.
Why? Because success is about software delivery velocity and quality. Realistic, but fictitious data is great for that. It allows you to protect the data at the same time.
For the quantitative use cases, it is not whether the data is completely real. It’s whether valid statistical analysis can be performed across the dataset to draw insight from it. Properly using realistic, but fictitious data is great for helping you draw insight. And it can protect sensitive data domains at the same time.
2024 Masking Insights: Revealed and Analyzed by the Delphix Experts
How are you protecting sensitive data in non-production environments? In our recent State of Data Compliance and Security Report, 66% cited use of static data masking. Discover other masking insights, including how to use masking for data compliance — without making trade-offs for quality or speed!
Watch the on-demand webinar to learn more.
How Delphix Data Masking Is Better Than Encryption
For your key use cases, leveraging a data masking solution such as Delphix Continuous Compliance can help you achieve incredible software delivery speeds for development and testing. Delphix gives you the right balance between protection and insight for your quantitative needs.
By using Continuous Compliance, you can irreversibly mask data — transforming real data into fictitious data that is realistic. This masked data is representative and maintains referential integrity across a variety of heterogeneous environments.
And importantly, you can conduct data masking at enterprise scale and velocity. Delphix has helped many customers take their deployment frequency from the low range to the elite range. And, we have customers who are masking 1000s of datasets covering PBs of data.
For example, Channel 4 used to take days to refresh 18TB of data in test environments. This inhibited agility. Since adopting Delphix, refreshing now takes minutes. At California State University, they can now execute 2,000 requests/month in minutes. And at Delta Dental, they can now mask data and deliver virtual data copies to a team of 200 developers in minutes.
For customers like these, choosing Delphix makes achieving compliance, speed, and quality possible. You, too, can do the same — without any trade-offs.
Eliminate Data Risks and Deliver Compliant Data
Sensitive data sprawl increases data risks, from security to regulatory compliance. Delphix eliminates these data risks by automating data masking for compliance. This includes masking personally identifiable information (PII) for compliance with standards from GDPR to HIPAA.
Remove Data Bottlenecks and Deliver Speed at Enterprise Scale
Inefficient data compliance processes slow down innovation. Delphix delivers speed at enterprise scale by automating the delivery of masked data to downstream teams, including development and testing. This allows you to accelerate innovation without sacrificing compliance.
Improve Software Quality with Consistent Masking
Poor test data practices impair software quality and reliability. Delphix consistently discovers and masks sensitive data, ensuring data utility and referential integrity with production data. Downstream teams gain access to realistic, masked test data quickly. As a result, software quality improves, without slowing down development or hindering compliance.
Get Started with Delphix
See for yourself why Delphix data masking is the right choice for your team. Request a no-pressure compliance demo today. You’ll find out how masking with Delphix enables you to gain compliance, speed, and quality — without any trade-offs.