With an emphasis on the importance of data also comes the importance of safety and security. That’s where data masking comes into play. Data masking is a security technique that scrambles data to create an inauthentic version for non-production purposes. Masking retains the characteristics and integrity of the original production data, helping minimize security issues while using information for analytics, training, and testing. Here are some of the ways in which companies are using masking to the benefit of customers and inner workings alike.
Common Data Masking Methods
Among the leading data masking examples is the substitution method. This takes the original data value in a record and replaces it with an inauthentic value. For example, a company may replace every male name in their database with a standard value, and every female name with another value. This makes sure the format of the inauthentic data is precisely the same as the original data. There’s also the shuffling technique, where the values are vertically shuffled in a column of a database table. This can be used by a banking system to shuffle account balances so that authentic data is not easily accessible.
In data masking, there is also the method of averaging, replacing all numerical values in a table column with an average value. This is also used in banking to allow for data privacy among individual accounts. Redaction is the most straightforward method of data masking, replacing sensitive data with a generic value like “X”. It’s a common practice to mask credit card numbers while nulling is a similar practice, putting NULL in the data field like you would for a social security number. Lastly, there’s format-preserving encryption, which turns data into an unreadable array of symbols while maintaining the structure of authentic data.
Data masking is all about having an effective method to protect sensitive information, while also not inhibiting workflow. In static data masking workflow, a copy of the original data is made, and masking is applied to that company. There are two popular methods of static data masking: extract-transform-load (ETL) and in-place masking. ETL extracts data from a production database then apply masking before loading it into a test database. In-place masking utilizes the high-end facilities of a production database to make sure that a working copy functions within the same database.
Beyond those methods, there is also dynamic data masking, in which is a mask is applied on a copy of the data whenever the system receives a user request. Organizations may also opt for view-based data masking, where a user requests information based on their access right, with a mask then being applied. The user will get that masked view of the original data. This is common for test environments. Finally, there’s proxy-based data masking, a newer method of masking in which all the data requests go through a proxy system eliminating an overwhelming number of queries to protect against hacking or any unauthorized access.
General Rules of Data Masking
There are general rules that organizations need to adhere to in order for their data masking protocols to be operational. Data masking must not be reversible, and the data must be represented as to not alter the nature of this sensitive information. Data masking should use the transformations in such a way that geographic distribution, gender distribution, readability, and numeric distributions of the original data are preserved.
Masking methods are not for all data. The existence of so-called “dummy data” will provide coverage through a masked copy of sensitive data, while not compromising integrity. Regardless of the type of mask, some data can remain a matter of public record as it is not sensitive in nature. Secure access and safety in workflow operations are all that companies need to focus on to avoid the cost of a data breach.