With businesses across the world embracing digital transformation projects to adapt to modern business requirements, a new challenge has emerged – the increasing usage of data for business-critical functions and protecting the sensitivity of its nature. Within the same organization itself, multiple functions use data in various ways to meet their objectives, adding a layer of complexity for data security professionals who aim to protect the exposure of any sensitive data, but also want to ensure that it does not affect performance. This sensitive data can include employee and customer information, as well as corporate confidential data and intellectual property that can cause wide ramifications by falling into the wrong hands. For organizations that depend on high-quality data for their software development processes but also want to ensure that any sensitive information contained within it is not exposed, a good static data masking tool is a crucial requirement for business operations.

Protect data in non-production environments

A critical aspect of data protection is ensuring the security of sensitive data in development, testing and training (non-production) environments, to eliminate any risk of sensitive data exposure. The same protection methods cannot be used for production and non-production environments as the requirements for both are different. In such cases, de-identifying or masking the data is recommended as a best practice for protecting the sensitive data involved. Masking techniques secure both structured and unstructured fields in the data landscape to allow for testing or quality assurance requirements and user-based access without the risk of sensitive data disclosure.

Maintain integrity of secured data

While securing data, it has also become important for organizations to balance the security and usability of data so that it is relevant enough for use in business analytics, application development, testing, training, and other value-added purposes. Good static data masking tools ensures that the data is anonymized in a manner that retains the usability of data while providing data security.

Choice of anonymization methods

Organizations will have multiple use-cases for data analysis, based on the requirements of the teams that handle this data. In such cases, some anonymization methods can prove to have more value than others depending on the security and performance needs of the relevant teams. These methods can include encryption, tokenization or masking, and good tools will offer different such methods for anonymization that can be used to protect sensitive data effectively.

For years, Mage™ has been helping organizations with their data security needs, by providing solutions that include static data masking tools for securing data in non-production environments (Mage Static Data Masking).
Some of the features of the Mage Static Data Masking tool are as follows:

• 70+ different anonymization methods to protect any sensitive data effectively
• Maintains referential integrity between applications through anonymization methods that gives consistent results across applications and datastores
• Anonymization methods that offer both protection and performance while maintaining its usability
• Encrypts, tokenizes, or masks the data according to the use case that suits the organization

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