Healthcare, finance, telecom, eCommerce… These are just a few of the industries that run on sensitive data. And where sensitive data flows, the risk of exposure follows. A single breach can mean operational disruption, significant financial losses, and years of hard-earned customer trust gone overnight.
None of a business wants to end up there. Which is why security and responsible data management have become a top priority for ventures of every scale and niche. This perfectly reflects the stats from Fortune Business, which forecast the global data protection market to reach $656.47 billion by 2034.
Key Highlights
- Shuffling provides full dataset visibility to analysts, while assigning real values to the wrong people, making individuals non-identifiable.
- The substitution technique replicates a realistic dataset for testing, but any inaccuracies in your real dataset will directly impact the quality of the simulated one.
- Tokenization ensures that sensitive records never travel across your systems as is, only the token does, keeping real values locked safely in the vault.
- AI automates the entire data masking process, from spotting sensitive variables to applying the right technique, thus minimizing errors and speeding things up.
We believe you also recognize the importance of data protection and, perhaps, have already employed some approaches and technologies, such as cybersecurity and blockchain, to elevate your system’s safety. But even with these advanced solutions in place, your data may still end up in the wrong hands. That is why you need an approach that makes sensitive variables useless for intruders, while keeping them valuable for internal use.
We know what fits the bill perfectly: data masking. Let’s dive right in and unpack it from every angle.
Your Last Line of Defense: What Data Masking Really Does for Your Business
Probably, the name gives it away, but let’s get a brief definition of data masking out of the way before we begin. Data masking is a technique that creates a similar but inauthentic version of your business and customer information. These values mean nothing to intruders, but remain meaningful and actionable for your staff and third-party vendors.
For example, by masking a customer’s card number in your fintech system as **** **** **** 9089, it makes no sense for hackers. But if the client reaches out to your contact center, those last four digits are more than enough for your agent to provide assistance.
Now, let us guess your next question. Does every business need to mask its data? It all depends on the industry and the venture’s size. Based on our decades of experience working with diverse partners, we can confidently state that for enterprise-scale ventures managing huge volumes of information daily, data masking is not optional.
Looking for an expert in enterprise software development?
For some industries like healthcare, finance, and retail, this approach is almost always a must, regardless of business size. That is because they collect users’ personal information and must strictly adhere to industry-specific regulations.
Tap to explore more reasons to Double Down on Your Patients’ Data Security
On top of that, you should employ data masking if you are going to integrate AI technology into your solution. You see, algorithms should be trained with private data to deliver the desired results. And as they contain such sensitive variables, they often become a tasty morsel for hackers. Masking simply solves this issue, allowing you to train AI models with the required data while keeping it completely secure.
Data masking and obfuscation are related, but not the same thing. Obfuscation is the broader term, referring to all techniques used to make variables unreadable for unauthorized parties. Meaning, data masking is just one of the methods within that broader obfuscation umbrella.
Time to Mask Up: Where Data Masking Makes All the Difference
Do you need to mask all the data across your system? Or can you apply it only to specific operations and use cases? Everything is pretty simple: mask your data where the real risk lies. Now, let’s break down the core cases where you should definitely hide crucial variables, still keeping them meaningful and actionable for those who need to work with them.
- Software testing: Your development team and QA engineers can hardly test a product without data. At the same time, they don’t need real user information. Variables that resemble the original are pretty much enough.
- Sharing with third parties: Your operations may require sharing data with vendors, outsourced teams, and other third parties. All of these can become a weak point in your security chain. Instead of sharing a full dataset, provide only the portion that’s actually needed. For example, if you’re working with a logistics partner, disclose address fields and mask other personal records like payment details, phone numbers, and emails.
- Analytics & reporting: Partial variable masking is also highly applicable for analytics purposes. Your team needs patterns, not personal information. For instance, if you want to identify disease patterns among a specific patient group, there is no need to give analysts the entire dataset. Simply mask patient names, addresses, and other personal details, giving visibility only into age, gender, and disease-related columns. That is more than enough for robust data analytics.
- Cloud migration: Data becomes highly vulnerable during cloud migrations, mainly because it moves across multiple environments, systems, and third-party platforms, which are actually potential entry points for cyber criminals. With this in mind, it’s definitely worth masking variables before migration.
- Employee training and product demos: Most businesses also use data masking to train new employees and run product demos. Instead of using real customer records, sensitive fields are replaced with realistic values. For example, names are changed, and emails are anonymized. You have enough variables to show how the program runs and works, what key features it has, without compromising private information.
In software development and testing, data masking assists in creating realistic yet fake datasets that are sufficient to test and validate product performance. In other words, these variables are enough to test features in real-world scenarios and spot bugs, but they are useless for intruders.
One Mask Doesn’t Fit All: Breaking Down Data Masking Types
Given that businesses have different purposes for masking data, there are several types of this approach to consider. Let’s now take some time to review the most common ones worth your attention.
Data Masking Types | |||
|---|---|---|---|
| Type | Description | Example | Use Cases |
| Static | Permanently replaces sensitive data in a copied dataset before sharing with outside production | Real data: Katrin Brown, brown@email.com Masked: Alex Pirs, user@test.com | Testing, development, third-party sharing |
| Dynamic | Masks sensitive data in real time based on user access | Admin sees: Katrin Brown, brown@email.com Support agent sees: K**** B****, b****@email.com | Customer support, admin panels, internal tools |
| Deterministic | The same data is always replaced with the same value, keeping it consistent across systems | Katrin Brown is masked as Alex Pirs across all systems, from orders to payments | Cross-system data sharing |
| On-the-Fly | Data is hidden temporarily while moving between systems, but the masked version is not stored | System A: Katrin Brown, brown@email.com During transfer: Alex Pirs, user@test.com System B: Katrin Brown, brown@email.com | Data transfers, system integrations, cloud migrations |
| Randomized | Data is replaced with random values with no consistent pattern | Real: Katrin Brown First time masking: Alex Pirs Second time masking: Emily Stone | Analytics, reporting, testing environments |
How Does Data Masking Actually Work? Core Techniques Explained
Now that we have covered the core use cases and types of data masking, it is time to get into the nuts and bolts — the techniques that actually make it work. There are several approaches you may come across. We have shortlisted the core five, each with its own benefits and pitfalls, so you can keep an eye on the best fit before diving into the data masking process.
1. Shuffling
Discover more advantages to reap from Insurance Data Analytics
In this case, your team requires a dataset including sensitive information like addresses, income levels, property details, credit scores, etc., to spot patterns and build accurate models. However, they still don’t require knowing exactly who that data belongs to. And that is precisely where shuffling steps in. This technique is all about assigning real inputs to the wrong people.
As such, private records belonging to Sarah Geller get assigned to Nick Green, and so on. This way, you give the analytics team a completely realistic dataset to work with, without compromising any sensitive information.
Need a custom insurance solution to optimize your operations?
2. Substitution
See how we designed an Intermodal Shipment and Container Tracking Software
3. Character Masking
4. Hashing
Data hashing can also come in handy to meet regulations like GDPR and CCPA, which require permanently deleting personal information upon a customer’s request. The thing is, some accounting laws require you to retain certain records for up to 5-7 years. Quite a dilemma!
Hashing sorts it all out in one fell swoop. Instead of deleting the entire client profile, it assists you in simply hashing their personal details and keeping only the transactional records.
5. Tokenization
When the customer fills in their card number at checkout, your system immediately replaces it with a token like TKN-8876-TYLH-5543. The real card number is securely stored in the token vault.
Dive into our guide to discover How Tokenization Works In the Blockchain World
Sensitive data masking is a combination of techniques aimed at protecting private information. Masking variables means making it useless for hackers but meaningful for your team, partners, and external workflows. Here are the core private records businesses need to hide:
- Personal details
- Financial data
- Medical records
- Insurance information
- Login credentials
The Next Frontier: AI-Powered Data Masking Steps In
Talking about data masking, it would be a huge gap if we overlooked the role of AI in elevating this data protection approach. Specifically, AI tools assist in automating the entire process, from spotting personal information to applying the most relevant masking methods.
Just imagine how much time your team would spend manually identifying and grouping sensitive financial records across an enterprise-scale system with thousands of data points.
Not to mention that manual processes are never free from errors. If your team unintentionally overlooks hiding some private records and it ends up in the wrong hands, you may face heavy fines due to regulatory violations.
Meanwhile, machine learning algorithms can instantly detect sensitive data across massive datasets. NLP, on the other hand, can quickly replace it with realistic alternatives. In addition, AI tools are capable of masking data in real time during migrations, integrations, or live transactions. Thus, ensuring your variables remain secure at every touchpoint.
These are just a couple of possibilities of AI-powered data masking. AI capabilities continue to evolve, and we will likely be surprised by the solutions it will offer for data protection in the future.
Ready to Mask Your Data?
Given everything we have covered about data masking, you probably have no doubt about its importance. Ideally, it should be an inextricable part of your data management strategy, ensuring your variables remain useless to intruders, yet valuable for your team.
But choosing the right technique and applying it properly is not as simple as it may seem. With years of experience working across different industries and ensuring peace of mind about data security, our experts will be happy to assist you as well.
Just give us a call, and we will turn your project into a success story.
About the author
