3 Reasons Why Organizations Should Use Data Masking

 In order to minimize the excessive spread and disclosure of confidential data within an organization, data masking, often known as pseudonymization, is used. Data masking replaces real data with usable fictional data so that it can be safely used in cases where there is no requirement for actual data.

Many types of sensitive data can be shielded with data masking. For example:

• Protected health information (PHI)

• Personally identifiable information (PII)

• Intellectual property (ITAR and EAR regulations)

• Payment card information (PCI-DSS regulation)

Through the data masking solution, data values are altered while data formats remain unchanged. Data masking uses many techniques to modify confidential data, including the replacement of characters or numbers, the shuffling of characters, or the use of random data generation algorithms that have the same properties as the original data.

Here are three key reasons why enterprises should incorporate data masking in their broader data security strategy, considering the high priority need for organizations to protect their sensitive data.

1) Safeguard Non-Production Data

Although allowing confidential data to be shared/copied/used safely, masking helps you safeguard certain data sets and fulfill enforcement requirements without hindering your business operations.

If left unregulated, contractors or offshore workers may access production data in non-production environments and potentially transfer it across locations through the cloud or removable media. And at large, there may be more than one such data set. As long as the data remains available for purposes of non-production, masking will monitor the distribution of actual data that could be prone to a violation or outright theft. It also decreases the potential threat surface of your company.

2) Defend From Insider Threats

Trusted workers, developers, trainers, business analysts, who are already inside perimeter defenses, may have a necessity to access data but may not necessarily need access to actual production data. The real danger to the companies of today can potentially be from within; the extent of threat from insiders should not be underestimated. Internal issues such as errors, unintended employee acts, third-party snafus, and stolen computing devices account for a large proportion of data breaches.

Organizations release the data workers need to get their work done by masking confidential production data while reducing the possibility of a malicious, reckless, or compromised insider breach of data.

3) Comply With Regulations

Many data privacy regulations have been enacted in the past. Many more are in the offing. These laws – comprehensive in nature – require enterprises to ensure complete data security. SOX, GDPR, CCPA, etc. are a few examples of such regulations. A data masking solution helps comply with these regulations.

Two main principles are presented by the GDPR: data minimization and pseudonymization as ways to protect the privacy rights of people while allowing data controllers to use collected data for other purposes.

GDPR requires organizations to enforce data minimization, which is to gather and use data limited to what is required for a particular reason, to maintain it no longer than appropriate, and not to make it accessible to an unlimited number of individuals. In order to satisfy the data protection criteria of GDPR, data masking can also be used.

Conclusion

Data masking, especially in non-production data environments such as application development and testing, training, and analytics, is a means of pseudonymised data. A reliable data masking solution helps organizations comply with key data privacy guidelines by replacing sensitive data with realistic, fictional data.