Data Masking is one of the best methods to reduce any kind of unnecessary exposure to sensitive data within an organization.?
Data masking is all about replacing real data with unreal features that look like real data as a disguise.?
It could be used across in a lot of situations where the access to actual data isn’t needed at all.
Data masking is able to protect a lot of forms of sensitive data and it especially includes:
- Protected Health Information. (PHI)
- Personal Identifiable Information. (PII)
- Intellectual property.
- Payment information.
- Transactional information.
Data masking can alter sensitive data that includes character or number substitution or the use of algorithms for the sake of generating random data that has the same properties as that of the original data in its original state.
It is such a process that?s quite popular among such users who work with sensitive data or perform research on sensitive projects. Data, when asked for, goes through a number of systems that could eventually pose a risk for the data to be misused or even, stolen.
Data Masking involves the process of redacting, a common procedure that forms a masked layer over the important elements of the whole data set. However, the same process is often irreversible.
Just like redacting, processes like de-identification are quite popular for identifying all kinds of information in the data set. These processes are known to prevent the identification of data even by the ones who have direct access to the data and are conducting the research in the first place.?
Masking is a method that’s commonly used in internet solutions, at least in 2020. Even the most brutal data attacks find it hard to read the information under data masking.
Data Masking: How it works?
Data Masking works on the process of ETL. Extraction-Transformation-Load or ETL is a description of the classic masking process that?s very abundantly used across a lot of platforms.?
Referred to as ?static? masking at times, ETL works effectively against any kind of a fixed export from the source repository.
Every phase is performed on a particular server, a source data repository, and a masking server which eventually is followed by a destination database.
A masking server is able to establish a connection to the source which further retrieves a copy of the data and applies the mask to the entire data. The result is finally loaded onto the target server.?
Here’s data masking an explained process
Extract, being the first step is just what it sounds like. The data is extracted from the storage repository which is usually formatted to make it easier for the mask to be applied.?
Usually, an extraction could be performed with a simple SELECT query that could be issued against the database and filters out unwanted rows and formatting columns.
The results could further be streamed to the masking application for processing. This data is then transferred securely like an encrypted file or over an encrypted SSL connection to the destination platform.
In the process of data masking, the second step is to apply the mask and transform the sensitive production data into a rough version of the original content.
Masks are almost always applied to columnar data which implies the data that’s of the same type and is grouped together.
A common example is a database that may contain a customer table where every customer’s entry includes a unique SSN or any such confidential information.
Such kinds of values are joined together and grouped in a single database which makes it easier for the masking application to identify any type of data to mask.
The masking application also parses through the data which masks each column and replaces every entry in the column with a masked value.
Loading is the last step that is involved in the data masking process. The masked data here is loaded into a destination database where it’s copied to one or more destinations and further loaded back into the tables.
The destination database won’t have sensitive data, which doesn’t make it subject to the same security requirements as compared to the original database with the unmasked data.