One of the most sensitive areas of your application or database from a data security perspective is user data—a high-value target for attackers. It can have lasting consequences for your organization and users, which is why data privacy regulations are crucial. Here is what you need to know about data masking tools, along with the most effective techniques for protecting sensitive information.
Data masking is the process of modifying the original characters in a dataset to prevent exposure or compromise, ensuring data privacy. It’s an essential method for protecting personally identifiable information, financial records, health information, and other sensitive data.
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.
There are two different types of data masking that most often apply to the digital landscape, which are common data masking techniques :
Data masking isn’t just another security tool you can add to your arsenal—it’s necessary to protect your users. Here are some reasons why 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, which is why it’s critical to mask sensitive information effectively. Data masking tools enable you to protect sensitive data stored in a more realistic way.
If your application handles sensitive information such as social security numbers, credit card numbers, or private medical records, data masking is essential to ensure compliance with privacy regulations. In addition to keeping sensitive information, such as protected health information, from falling into the wrong hands, it can also protect your company from liability for damages due to a data breach by helping prevent the breach.
The cybersecurity landscape spans a range of data encryption regulations and, in turn, multiple tools designed to protect sensitive information. Because they are not open-source and offer more context-aware protection capabilities, entity-based data masking software is often the better choice for organizations that need a more sophisticated approach to data masking.
That’s why some of the best data masking tools use multiple layers of protection for user data, all with the true, unbiased randomization that software can offer, often generating realistic test data for development purposes.
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, making it a key aspect of data management. By making this masking solution part of your comprehensive plan to protect user data, you can prevent and mitigate potential attacks before they occur.
At a glance, data-masking tools replace sensitive information within your given dataset with false but realistic-looking data types. 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 using a fixed set of rules before storing or sharing it, using 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 consistently to data for complete coverage across all 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 your threshold can only see a masked version of the data, with obfuscation rules in place.
Dynamic masking is primarily used as a role-based security measure in 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 data-masking approach, especially in non-production environments, the process runs automatically without storing 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 within the same row, table, or database, or across instances, particularly for sensitive information like cardholder data. It replaces sensitive data with non-sensitive but realistic equivalents as part of its data masking capabilities, providing an additional layer of protection while maintaining consistency and integrity.
However, since the same input always yields the same masked volume, deterministic masking can be reverse-engineered by an enterprising attacker, much as 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 to maintain data uniqueness. This type of masking introduces randomness into the process, making it harder for an attacker to decode the data and use it for their own purposes.
Non-deterministic data masking is ideal when adding layers of anonymity without consistency, significantly reducing the risk of data loss. This is an ideal way to mask data such as phone 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 a typical database, it could involve masking customer contact information using random lookup tables. Working with the right data masking tools can make this easier to execute, especially for test data, while still 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 appear to be 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 begin 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 data masking technique, but some developers and security experts find it less favorable because it can compromise data integrity and make testing more difficult.
This form of data masking is particularly applicable when numeric or chronological data needs to be hidden, such as health insurance portability records. For example, some security professionals use it to hide financial transactions and their dates of occurrence or to run data analytics.
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 as long as 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 within the same system makes your database harder to break into, whether an attacker uses decompilation tools or works manually.
To help you choose the right data masking solution, here’s a ranked list of the top tools available today based on features, use cases, and industry fit.
Best for: Code-level protection and obfuscation
Features:
Best for: Enterprise-level compliance and scalability
Features:
Best for: Large enterprise databases
Features:
Best for: DevOps and CI/CD pipelines
Features:
Best for: Native Oracle stack users
Features:
Best for: Real-time and entity-based use cases
Features:
If you’re trying to do your own data masking and tool comparison to find what’s best for your application, it’s important to remember that a program with multiple protection types 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. Dotfuscator, DashO, and JSDefender mask and encrypt data in numerous programming languages across desktop and mobile applications of all types.
These are just a few of the ways PreEmptive’s tools enhance your data protection efforts:
Our code obfuscation, encryption, and data masking tools help keep the information in your database from falling into the wrong hands, but user training is also crucial to enhancing data security. Request a free demo of PreEmptive’s suite of tools today to see how these encryption tools can make your data safer.