The advanced technique of data masking focusses on obscuring sensitive data while copying datasets. DATA MASKING is the process of hiding original data intelligently with modified content.
This useful technique is widely popular among companies worldwide. The industry will touch $435 million within 2025 with 15% CAGR. These varied modifications of data are authentic and characteristically unbroken for the completion of the masking technique uploading the restrictions of the original data.
The benign replica of the original data is used for different purposes such as training or prototype testing. This technique is very useful for the industries in which original data needs to be secured and protected from external overexposure.
The original data is concealed with the help of data masking techniques for the testing of administrative and operational compliance.
Types of Data Masking
The data masking process needs to evaluate the sensitive part that needs to be obscured and concealed.
The amount of data needed to be protected and their location determines ideal techniques for data concealing. A single technique of data masking can be used for an entire organization, however, it needs proper planning and selection of the technique.
Static Data Masking
This kind of data concealing is static. The importance of static data masking is that in this technique the data is concealed under the original database environment.
This means that data masking is done in two basic steps – first, the original data is duplicated under test conditions, and then it is shared with the vendors to carry out the masking process.
The last and final step includes masking and extraction of data which is again transferred under test conditions. This process of data masking is not considered to be the ideal one, however, static data masking is appropriate for organizations that depend on third party services and external consultants.
Dynamic Data Masking
The dynamic data masking procedure depends upon the IT consulting and automation techniques for the concealing and obscuring of sensitive data. The sensitive data is secured in real-time to prevent privacy breaches.
The authentic data is restricted for sharing with all the users who can access the entire database for dynamic data masking. Rather, the data is jumbled and reversely proxied by using several dynamic masking tools.
This technique is estimated to grow at a higher CAGR of 8.5% due to its reduced risks for data breaching. The original data becomes inauthentic and the users accessing the data for unmasking is not exposed to see the authentic version of the data.
However, this advanced technology has some drawbacks. The reverse proxy consumes a high amount of time and money. Furthermore, this proxy technique does not guarantee the complete prevention of security issues.
On–the Fly Data Masking
The businesses of North America have a market share of 38.95% for this data masking process. Both dynamic data masking and on-the-fly data masking are carried out on demand.
For successfully carrying out this kind of data concealing, the organization includes Extract Transformation Load (ETL) for concealing the sensitive information present in the authentic database.
On-the-fly data masking makes sure that the process is carried out inside the database application. Much different agile organizations benefit from this kind of technique as it is less time consuming than others.
Customary Data Masking Techniques
There are some common types of data masking techniques, which are known to be employed by different organizations for camouflaging sensitive information.
● Character Scrambling: The originality of the data is covered up by random data rearrangement.
● Data Variance: The datasets are reorganized based on the mean and variance of statistical analysis.
● Encryption Algorithm: Complex data and information can be encrypted with an authorization key or code.
● Nulling Out: Database is made to be null for unauthorized access.
● Substitution: The sensitive data is substituted with a modified version to hide them.
● Shuffling: It incorporates one dataset for another within the database and is similar to the substitution process.
Data masking conceals sensitive information of the original data with selectively modified versions of data while the database management process takes place. Data masking is accompanied by reverse-engineering processes for providing credibility to the obscuring process regarding hiding sensitive data.