Data masking best practices
At this point in time, it’s no breaking news to us that a data breach or hack has been discovered in a company.?
Data breaches happen every now and then, which not only makes the security of our firm or company questionable but also makes us realize that maybe our security systems aren’t as flawless as they should have been.
To our surprise, the existing tools that we have, allow us to neutralize the same data breach attempts.
The protection of sensitive information has been the most common goal for every company and now, with techniques like Data Masking, it seems possible.
Data masking strategies (when done right) are a proven method to stop the hackers and attackers from breaching into our security systems.
Data masking, as we know, replaces the sensitive data with unreal yet realistic values of the data where confidential information like names, credit card numbers, addresses, etc are rendered. However, the masked data is still much valuable for application development, analytics, and testing.
DATA MASKING: AN EXPLANATION
Data masking is a modern concept that technically aims at preventing the abuse of sensitive data by giving the users unreal data rather than real, sensitive data.
This method involves the application of transforming algorithms and other techniques that replace the real data with fake data so that any kind of breach isn’t going to result in any kind of potential data loss.
When data scientists apply the technique of data masking, it could avoid the exposure of sensitive data while still being able to analyze and interpret the data.
Data masking eventually reduces the visibility of data to unauthorized parties. Data scientists need to ensure a balance so that the sensitive data is protected without even masking so much that the data becomes difficult to analyze.?
Data security and privacy regulations, on the other hand, are also aiming at the protection of sensitive data within organizations of all sizes.?
Organizations now are responsible for concealing a variety of sensitive data, that includes:
- Protected Health Information. (PHI)
- Personal Identifiable Information. (PII)
- Intellectual property.
- Payment information.
- Transactional information.
These regulations also have restrictions on how long personal data could be stored.
Along with that, it also limits international sharing, data minimalization etc.?
This leads to data scientists being more thorough and diligent with data masking.
Data masking best practices for protecting sensitive information
Data scientists are supposed to follow all kinds of security practices. These include password protection, working in protected environments and using strong authentication because once data is allowed to be accessed by an individual, its security becomes his responsibility.
For safe practices of data security, data masking is used and there are a few things that the data scientists need to keep in mind:
Using strong masking algorithms – Either strong masking algorithms or different complex character substitutions are the best methods to ensure that your data is being masked properly.
The more diverse and secure it is, the more irreversible it is.
Mask sensitive data – There?s no need to mask everything when it comes to data masking. Data varies, yet only a few columns of the dataset are worth saving, right? There?s no need to utilize all your resources on masking data that does?t even need to be masked.?
Maintain the same data structures overall – And when we talk about maintaining the same data structures, it doesn’t include structures only. Rather, it also includes the same relationship between the rows, columns, and tables. This helps in the preservation of relationships between values after the data is masked.
Test the outcomes – When you are testing your data, if you don’t get the desired results, try to restore the data to the state it was originally in and tweak the masking algorithms so that the irreversibility process doesn’t result in a loss.
A recurring process – In data masking, you should be able to repurpose the same for other similar datasets as well.?
This allows the masking of any kind of data set as well that is added to the database.
Maintain referential integrity – Whatever sources you are deriving your data from, make sure that the data is masked all the way so that the same relationship between values is preserved after the transformation of data.?
Data masking is and will be one of the most important security measures against cyberattacks of all kinds.
This means as we progress, data scientists are going to continue working on stagnant data masking processes that would be far more helpful than what we are witnessing right now.?