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How Data Masking Can Help Secure Your Data

Since the business field has been adopting cloud computing lately, many businesses have shifted to cloud computing because of the various benefits it provides them with. Moreover, security is a very important thing to keep an eye on, especially in business applications which involve security of your data. Storing your data on premises can lead you towards data breaching attacks.

By using cloud services, you can securely develop and test various things without losing important data. So, how can companies provide their employees with easy and secure access to data without any risks of data breaches?

This is where Data Masking comes in. It is a technique which provides you with enough security when storing data in cloud. It works by using fake data (which looks real) in the place of real data before actually storing it in cloud. Companies and services like snowflake data masking allows businesses to take benefit from the cloud technology without compromising data integrity.

Benefits Of Data Masking For Businesses

Here are some of the benefits of data masking.

Dynamic Masking

In this technique, the data is masked right when the request is made. Let’s discuss two types of data masking.

There is another technique which is in use these days, it is called Query Substitution. In this technique, the query make by the user is redirected to retrieve data from masked data storage. These queries are really effective and precise, and can help you retrieve data from remote locations easily.

The cloud infrastructure has recently been exposed to various security threats, but companies can’t afford to avoid using the cloud technology as it provides them with a plethora of benefits as well. Moreover, techniques like data masking is being developed to make the use of cloud technology safe and secure. This also doesn’t affect the flexibility and functionality of your cloud infrastructure. So, I’m a nutshell, data masking has helped in making cloud computing safer and more secure than ever.

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