As more and more organizations adopt data masking techniques, it’s important to stay ahead of the curve and understand the future of this practice. Masking protects the privacy of individuals and businesses and ensures regulatory compliance. Masking also makes it more difficult for hackers to steal sensitive data, helping to reduce the risk of data breaches. It also improves the performance of analytics and business intelligence applications. Keep reading to learn what is data masking and what to expect in the future of masked data.
Data masks describe the process of obscuring data to protect it from unauthorized access. Masking can be applied to structured and unstructured data and is often used with other security measures, such as encryption.
There are several reasons why organizations might choose to mask their data. One of the most common is to protect against accidental or unauthorized disclosure. By obscuring sensitive information, businesses can ensure that it remains confidential and cannot be accessed by unauthorized individuals.
Masking can also be used for regulatory compliance purposes. Organizations that are subject to certain regulations, such as HIPAA or PCI DSS, may be required to take steps to protect certain types of data. Masking can help ensure that this data is not compromised, even if it falls into the hands of an unauthorized individual.
Masking is also used for testing and development purposes. When testers or developers need access to live production data, they can use a masked copy instead. This helps minimize the risk of compromising sensitive information while allowing them to perform their work effectively.
Centralized vs. Distributed Data Masking
Centralized data masking is a process where all the data in an organization is masked at once. Centralized masking can be done through various methods, such as software or hardware appliances. The benefits of centralized data masking include scalability, consistency, and the ability to reuse masks.
Distributed data masking is the opposite of centralized data masking. In distributed data masking, masks are created per application and applied to specific datasets. This allows for more granular control over access to information and can help improve performance by reducing the number of requests that need to go through the central system.
There are pros and cons to both centralized and distributed data masking models. Centralized systems can be more efficient regarding resources, but they can also be less agile than distributed systems. Distributed systems offer more flexibility but may require more manpower if implemented incorrectly.
Choosing a Method of Masking
When considering a method of data masking, several key factors exist. The first is the purpose of the data masking. Is it to protect sensitive information while it’s being processed or stored? Or is it to ensure that test data used in the development and testing scenarios look like real data?
The second consideration is how much of the data needs to be masked. If only a small amount needs to be obscured, then manual methods such as substitution or deletion may be sufficient. However, if large amounts of data need to be masked, then a more automated approach may be necessary.
Third, the type of data needs to be considered. Some types of data are easier to mask than others. For example, text-based information can easily be substituted with dummy text, but images or videos would be more difficult to disguise.
Finally, the security and compliance requirements for protecting sensitive information must also be considered when choosing a data masking method. Some methods may provide more substantial protection than others and may be better suited for specific industries or compliance regulations.
The Future of Masked Data
In the years ahead, data masking will continue to play an essential role in protecting sensitive data. However, there are several new developments that we can expect to see in the field of data masking.
First, we expect to see more organizations adopting data masking to comply with regulations. In addition, we can expect to see data masking being used more often for application testing and development. This is because data masking can help protect against accidental disclosure of sensitive information and also help ensure that test data is representative of real-world data.
Another trend we can expect to see in the years ahead is the increasing use of artificial intelligence and machine learning for data masking. This is because these technologies can help identify sensitive information and create masks for it. As a result, this will help organizations reduce the time and effort needed to perform data masking operations.