One of the most sensitive things in your application or database is user data—it’s a high-value target for attackers and can have lasting consequences for your organization and users. Here is what you need to know about data-masking tools, plus the techniques that are most effective for protecting sensitive information.
Data masking is the process of modifying the original characters in a data set to prevent it from being exposed or compromised. It’s an essential method for protecting identifiable information, financial records, health information, and other sensitive data that criminals can use to commit fraud or identity theft.
In practice, data masking creates fake versions of your application’s data by altering the confidential information within to something that makes sense but has no real-world value. This makes it impossible to reverse engineer or backtrack the information without access to the original data set.
There are two different types of data masking that most often apply to the digital landscape:
Data masking isn’t just another security tool you can add to your arsenal—it’s a necessity for protecting your users. Here are some of the reasons why so many developers rely on it for their application security posture.
Exposing even a few digits of a customer’s social security or credit card number can make it easier for a criminal to compromise all their most sensitive data. Data masking tools enable you to more easily protect the information this data stores.
If your application handles sensitive information such as social security numbers, credit card numbers, or private medical records, data masking is essential. In addition to directly keeping this sensitive information from falling into the wrong hands, it can also protect your company from being liable for damages due to a data breach by helping to prevent the breach from happening in the first place.
The cybersecurity landscape has a range of different data encryption regulations, and in turn multiple tools available from numerous entities designed to protect sensitive information. Because they are not outright open-source and provide more contextualized protection capabilities, entity-based data masking software is often the better choice for organizations that need a more sophisticated approach to data masking.
Anything a human developer can protect manually, a human hacker can find a way to compromise. That’s why some of the best data masking tools use multiple layers of protection for user data, all with the true, unbiased randomization software can offer.
Much like the other security measures you use to protect your app or database, data masking is instrumental for protecting your organization’s reputation and preserving trust with users and the public. By making this part of your comprehensive plan to protect user data, it’s easier to prevent and mitigate potential attacks before they can even happen.
At a glance, data-masking tools replace sensitive information within your given dataset with false but realistic-looking data. This practice obscures the original values in the set while maintaining its structural integrity.
Many data-masking tools also rely on identity and access management (IAM) rules to protect data. It often means providing limited access to sensitive data, in turn minimizing and fortifying the entry points and attack surface area of a database or application.
Here are some of the ways it can make a difference for your application, your organization, and your users.
Static data masking protects sensitive data with a fixed set of rules before storing or sharing it with data sanitization tactics. It’s ideal for data that remains consistently static over time and isn’t subject to frequent changes, or data that is typically kept at rest, such as that stored in a database or electronic health record system.
Security teams can predefine static data masking parameters and apply them to data consistently for complete coverage across all the environments you use. As a point of comparison, many data masking programs can easily and automatically set these parameters and implement them as an algorithm.
Dynamic data masking protects data at the exact moment users are accessing it. While your original data remains intact in your database, dynamic data masking only allows users with certain permission sets to see the unmasked data.
Anyone below the threshold you set can see only a masked version of the data with obfuscation rules in place.
Dynamic data masking is primarily used as a role-based tactic for customer support applications. This allows your support team to do their jobs while protecting medical records, credit card data, passwords, and other sensitive information.
Similar to dynamic data masking, on-the-fly data masking is a technique that masks sensitive data in real time while it’s being transmitted or accessed. With this version of data masking, the process happens automatically without needing to store the data in a masked format first. It works while the data is moving between systems.
On-the-fly data masking is ideal for data migrations, replication, or integration processes because it provides a safe layer of protection when your users’ data is at its most vulnerable.
Deterministic data masking is the process of replacing values in a column with the same value in a data set, whether it’s in the same row, table, database, or between instances. It replaces sensitive data with non-sensitive but realistic equivalents for an additional layer of protection with consistency and integrity.
However, since the same input always has the same equivalent masked volume, deterministic masking can be reverse-engineered by an enterprising attacker, much like an enigma machine. There is also a risk that the data could lose its anonymity, especially if there are correlations between the masked values and the original data.
By contrast, non-deterministic data masking randomly replaces data with different values in every processing instance. This type of masking adds randomness to the process so it’s more challenging for an attacker to decode the data and use it for their own purposes.
Non-deterministic data masking is ideal when you want to add layers of anonymity without consistency. This is an ideal way to mask data such as phone numbers or account numbers.
One of the most traditional forms of data masking, substitution replaces data with another value. Largely working with pre-established keys or rules, it replaces the original characters in the data set with alternatives.
As an example of what this might look like for the typical database, it could involve masking customer contact information with random lookup files. Working with the right data masking tools can make this easier to execute while still allowing it to work as an effective way of protecting data from breaches. It also minimizes the risk of human error.
Similar in principle to substitution, shuffling randomizes data within the same individual masking data column. While this may look like accurate data to a would-be attacker, it doesn’t reveal any vulnerable personal information.
However, much like substitution, shuffling has its vulnerabilities. For example, if your program uses a consistent shuffling algorithm, a hacker could start to understand it and reverse engineer your data through observation.
The process of nulling out data masks information by applying null values to a given data column so unauthorized users cannot see the actual data within. This is a relatively simple technique for data masking, but some developers and security experts find it less favorable because it can compromise data integrity and make it harder to conduct tests.
This form of data masking is particularly applicable when you need to mask numeric or chronological data. For example, some security professionals use it to mask financial transactions and the date when they take place.
Among the most complex and secure types of data masking, encryption uses a specialized algorithm that requires a key to decrypt. This process keeps the data safe provided that the key is only available to authorized users.
It also helps to have multiple forms of encryption tools in place. For example, using control flow obfuscation, string encryption, and renaming obfuscation all within the same system makes your database harder to break into, whether an attacker is using decompilation tools or working manually.
If you’re trying to do your own data masking tool comparison to find what’s best for your application, it’s important to remember that a program with multiple types of protection is best. That’s where PreEmptive comes in.
PreEmptive offers a range of code obfuscation tools that enable your team to take a proactive approach to data masking and add layers of protection. Across desktop and mobile applications of all types, Dotfuscator, DashO, and JSDefender mask and encrypt data in numerous programming languages.
These are just a few of the ways PreEmptive’s tools enhance your data protection efforts:
Our code obfuscation, encryption, and data-masking tools keep the information in your database from falling into the wrong hands. Request a free demo of PreEmptive’s suite of tools today to see how these encryption tools can make your data safer.