The Power of Data Masking: Safeguarding Your Data from Prying Eyes

Organizations usually implement the strictest security controls to safeguard their production data when it is stored and used for business purposes. However, certain instances, such as when data is being utilized for testing or training purposes or accessed by third-party entities outside the organization, pose a significant threat to data security. These instances can even result in compliance violations.

The Power of Data Masking: Safeguarding Your Data from Prying Eyes

When a data breach takes place, it’s a nightmare for the concerning organizations. This is especially true for small businesses, as history has shown us that at least half of them shut down operations within six months of an attack. That said, larger companies and agencies are not immune either and face serious consequences, including financial costs. According to the Ponemon Institute and IBM, the average financial cost of a data breach is a whopping $4.24 million.

Every department within a company is affected by a cyber incident in some way. A significant breach in a well-known company that handles sensitive data of millions of people makes the CISO look bad, as it appears to be a performance failure. It also reflects poorly on the CEO for lack of support. Overall, the business takes a hit not only on the financials but also on its reputation. So, what’s the solution? The answer is data masking.

How Data Masking Protects Sensitive Data

To protect data breaches, it’s important to establish policies and procedures that regulate access to sensitive data. One widely used method for safeguarding sensitive data is data masking. This technique creates data that is structurally similar to production data, but not identical. Once masked, this data can be used by application systems in the same way as the actual, production data. However, sensitive data values are not exposed to unauthorized parties.

PII or personally identifiable information refers to data that can be used to identify an individual directly or indirectly. Companies that handle this data must protect it from both external and internal threats as there are hefty penalties to be paid for non-compliance. Data masking is an effective method for safeguarding this data and can prevent data breaches and improve compliance efforts and project development.

Data masking makes most sense when you’re building data test beds for application development and testing. Many organizations simply copy the production data to a test environment, which creates a data test bed. However, this is a bad practice as it exposes PII data which should not be accessible to application developers. 

Test systems are often less protected than production systems, so here, additional precautions such as masking the data make most sense. You don’t want to expose PII to your programmers, such as salary information, phone numbers of co-workers, or customer contact information. Even worse, you don't want to expose customer credit card details to everyone. 

Data Masking Best Practices

To secure sensitive data through data masking, one must begin with data discovery, which entails understanding the data that is being held and distinguishing between different types of information with varying levels of sensitivity. Security and business experts typically work together to create an extensive record of all data components throughout an organization which requires a great deal of care and attention to detail.

Another key step in data masking is examining the circumstances in which sensitive data is stored and used. The security director is responsible for determining the availability of such data and deciding on the appropriate concealing strategy for each type of data. This process involves analyzing each dataset to determine the most effective strategy for concealing sensitive information.

For large enterprises, it is not feasible or practical to use a single data masking technique for all available datasets. Here, data masking actualization requires the careful analysis of each dataset. Data masking testing is thus also a crucial step in the data masking process. The testing teams must ensure that the results desired by the data masking strategies are being achieved. If a masking technique fails to meet expectations, the DBA must restore the database to its original unmasked state and apply a new masking procedure with new calculations. This step requires significant attention to detail and rigorous testing to ensure the security of sensitive data.

There’s no denying that data masking is a highly effective technique in protecting sensitive data. However, correct implementation is essential for it to provide high security and reliance. By combining different data masking techniques and doing comprehensive testing on the data used, the efficacy can be improved to a large extent.

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