Utilizing Masking to Protect Sensitive Data

You might have heard the term “data masking” before, especially in industries dealing with sensitive data like healthcare, finance, and IT. But what does it mean, and why is it important?

In simple terms, data masking is the process of hiding original data with random characters or data. This process ensures that sensitive information is protected from exposure to threats and unauthorized access. It’s like putting a mask on your data, where the mask shields the original content from view, but the data structure remains intact. This way, testing and analysis can still be performed without any risk of exposing sensitive information.

In the following sections, you’ll learn more about data masking, its significance, types, and various techniques. You’ll also discover practical ways to implement data masking at scale, the challenges you might encounter, and how to overcome them with dynamic data masking tools.

What is Data Masking?

Data masking creates a structurally similar but inauthentic version of your organization’s data that can be used for software testing and user training. This technique is beneficial when your organization needs to use functional but non-sensitive data. With data masking, you can protect your data from exposure while still being able to use it for various operational purposes.

Why does this matter? It’s because data masking can help prevent data breaches, which are becoming increasingly common in today’s digital world. As indicated by a Statista report, ransomware impacted 71 percent of businesses worldwide in 2022. By masking your data, you can keep it safe from hackers and other unauthorized users who may attempt to access it without permission. In other words, data masking is a proactive measure to secure your data from potential threats.

Whether it’s personal information like credit card numbers, social security numbers, or business-critical data, data masking provides a way to use and share data without exposing sensitive information. Regardless of the industry, every organization handles some form of sensitive data, whether it’s customer information, financial data, or proprietary research. Data masking ensures this information remains confidential even when used for development, testing, or analytics.

Moreover, data protection isn’t just about safeguarding your business. It’s also about compliance. Numerous legislations, like the California Consumer Privacy Act (CCPA), and the General Data Protection Regulation (GDPR) mandate that companies ensure the privacy of personal information. Non-compliance can result in hefty fines and legal action. Data masking helps organizations meet these regulatory requirements by providing a secure method of handling sensitive data.

Data Masking Types

There are several types of data masking, each with its advantages and applications. Here are some commonly used methods:

  • Static Data Masking: Static data masking (SDM) is a type of data protection that involves creating a sanitized copy of the production data. This method is typically used in non-production environments like development or testing. The primary advantage of SDM is that it allows sensitive data to be used realistically while ensuring it remains secure.
  • Deterministic Data Masking: Deterministic data masking (DDM) involves replacing sensitive data with fictional but realistic data. The same input will always produce the same output, making it possible to use the masked data across different systems while maintaining consistency.
  • On-the-Fly Data Masking: On-the-fly data masking is a method where data is masked in real-time as it leaves the database. This means that the original data never leaves the database, adding an extra layer of security. However, this method requires significant processing power and can impact system performance.
  • Dynamic Data Masking: Dynamic data masking (DDM) works by masking data in real time so that sensitive information is never exposed to non-privileged users. Unlike static data masking, DDM doesn’t create a copy of the data, but rather, it changes how the data appears to the user. This is particularly beneficial when users need to access the database for troubleshooting or generating reports.

Common Data Masking Techniques

Here are seven techniques that can be used for data masking:

  1. Data Encryption: Encryption is a common technique in which data is encoded into a format only authorized parties can read. It uses an algorithm to transform the data into a meaningless string of characters.
  2. Data Scrambling: Data scrambling, also known as obfuscation, is a method where data is replaced or rearranged to make it unreadable or hard to understand.
  3. Nulling Out: Nulling out is a technique in which specific data fields are replaced with null or empty values. This method is particularly useful for masking particular data fields that contain sensitive information.
  4. Value Variance: The value variance technique involves changing the value of data fields by a specific variance, such as increasing or decreasing numerical values by a particular percentage.
  5. Data Substitution: Data substitution is a technique for replacing sensitive data with fictional but realistic data. For instance, a real name might be replaced with a fake one.
  6. Data Shuffling: Data shuffling is a method where the data in a field is rearranged or shuffled. This technique is often used in conjunction with other data masking techniques.
  7. Pseudonymization: Pseudonymisation is the process of replacing identifiable data with artificial identifiers or pseudonyms. The pseudonymized data can’t be attributed to a specific data subject without using additional information.

Implementing Data Masking at Scale

The biggest challenge when implementing data masking at scale is managing the complexity of large data sets. With an increase in the volume of data that businesses collect daily, masking data to ensure its privacy becomes even more critical. Implementing practical tools like data protection platforms can help streamline this process by automating the data masking tasks, reducing human error and enhancing security.

A solid data protection platform can also support different types of data masking, such as dynamic and static data masking. It can handle structured, semi-structured, and unstructured data across multiple sources and environments. By providing a centralized platform for data masking, it can help organizations implement data masking at scale, efficiently managing the complexity of large data sets.

In conclusion, data masking is essential to your data protection toolkit. It allows you to use and share data without exposing sensitive information, helping you prevent data breaches, stay compliant with regulations, and maintain customer trust. By understanding the different types of data masking and techniques, you can choose the best approach for your organization’s needs, protecting both the business and your client’s valuable information.

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