Question 1

What is Data Masking Software?

Accepted Answer

Data masking is a technique used by organizations to protect sensitive data from unintended exposure. Data masking is also sometimes referred to as data obfuscation. There are a number of masking techniques, including data substitution, data shuffling, translating numbers into number ranges, nulling or deletion, character scrambling, and more. Companies use data masking software to shield sensitive data such as personally identifiable information (PII) or sensitive customer information while maintaining the data’s functional value.  
 Data masking software ensures that unauthorized people do not have visibility into real, sensitive data records by masking the data. Companies commonly utilize data masking to limit the sensitive data visible to their employees. This protects against both employee mistakes in handling sensitive data and malicious insider threat actors seeking to steal sensitive information. 
 For example, credit card numbers in a database can be redacted or replaced with false data in a billing software application so that the real numbers are not exposed and visible to frontline employees. A masked credit card number would be structurally similar and maintain the sixteen-digit credit card number format of "xxxx-xxxx-xxxx-xxxx" that the company’s billing software application expects this data to be in, while not providing the actual credit card number.  
 A common use case for data masking is providing non-production but realistic data for software development and testing. Applications must be developed and tested using real data to ensure the software meets the company or customer’s needs, but providing sensitive data to a development team exposes the data to people who don’t need to be authorized to view it. For example, if an educational software company is developing a solution to manage student testing data, having specific individuals’ testing information like real names, addresses, test scores, academic grades, and so on is not necessary to develop the tool. Having data based on real data but scrambled or obfuscated is sufficient to test the software. As long as the data is functionally correct, the software developers don’t need to know the precise, sensitive data to develop and test the software solution.  
 Data masking is most often for non-production purposes like software development and testing mentioned above, but it can also be used in production environments to control which users have access to sensitive information. For example, employees in a call center may need to look up a customer’s account information in a CRM software to process a payment but do not need access to the customer’s exact payment details like bank account and routing numbers to complete the transaction. The company must retain the actual bank account information to process the transaction, but this sensitive information does not need to be visible to the call center employee, so the company masks that data for the call center employee in their call center software application. 
 Other use cases for using masked data include: 
 
Sales demonstrations of software programs
User training modules
Sandbox experiments

Question 2

What Types of Data Masking Software Exist?

Accepted Answer

Static data masking 
 Static data masking solutions allow sensitive datasets to be masked while the data is at rest. This usually entails a complete copy of a masked dataset. Most commonly, this is used for non-production use cases like providing datasets for software development and testing purposes. 
 Dynamic data masking 
 Dynamic data masking solutions allow sensitive data to be masked while the data is in use, and the masking can be based on the attributes of the person viewing it. Most commonly, this is used for in-production use cases. For example, frontline employees or employees in a specific geographic area can view the sensitive dataset dynamically masked based on their role type in real time. This software can be particularly beneficial for customer service use cases.

Question 3

What are the Common Features of Data Masking Software?

Accepted Answer

The following are some core features within data masking software that can help users achieve their business goals: 
 Performance with large datasets: Data masking software must be able to meet the scale and speed of masking large datasets, whether the masking is performed on the database level itself, between application layers, or within the application itself. This is especially important for masking enterprise data and big datasets. 
 Preserving data characteristics: Some applications expect data to be in a specific format, such as a 16-digit credit card number. For masked data to be utilized in the application, the masked data must also conform to these data characteristics like number length. 
 Deterministic masking: Deterministic masking allows for the masked data to be consistently masked across multiple tables and applications. For example, if a data record has a first name of “Joan” then the masked name of “Claire” will appear consistently and uniformly across the masked dataset and applications it is used in. This is important especially for in-production customer service use cases where company employees interact with multiple applications like CRMs and billing applications to assist customers. Having consistently matching masked data in those disparate applications can aid in providing the best customer assistance. 
 Cloud-compatible data masking: Today, many companies are shifting from on-premises data stores to the cloud and are utilizing infrastructure as a service, platform as a service, and software as a service tools. Many data masking tools offer solutions to protect data regardless of where it is used.

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