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Leveraging Data Masking Technology to Protect Your Enterprise and Accelerate Development

Learn best practices for data masking to help build a secure data pipeline for your enterprise.
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Almost every day, we hear news about a new data breach. T-Mobile, the latest company to report a breach, announced that nearly 2 million customers’ personal information were impacted during the incident.

So far in 2018, there have been more than 2,300 publicly announced data breaches, totaling to 2.6 billion records exposed along the way, according to a new report by Risk Based Security. IBM’s latest Cost of a Data Breach report shows a typical breach can cost a company up to $3.86 million.

While hackers and malicious attackers are mostly to blame, organizations can do a better job protecting their data rather than inadvertently leaving their sensitive data vulnerable or exposed. 

This is especially true in cases where there is a handoff between development and testing teams or between offshore teams, where data may be shipped across borders. Data that leaves production needs to be in a form that can be disseminated for use among functional teams without worry of confidentiality breach.

Enter data masking. Data masking has become the de facto standard for removing sensitive data in non-production environments for testing store capabilities, customizations, configurations, integrations and more. 

But while data masking effectively protects sensitive information, the process of creating masked data is only part of the challenge. To make data masking both practical and effective, enterprises need a streamlined approach for delivering data once it has been masked, especially if it’s required to be moved into the cloud.

This is particularly true of enterprises with stringent requirements for moving data from a safe production zone into a separate non-production zone. This means organizations must efficiently execute the delivery process while ensuring that the transported data has been properly masked. 

Current methods often require complicated workflows and longer time to execute, consuming both infrastructure and labor. Hence, enterprises scale back masking initiatives or even abandon the use of masking tools entirely due to challenges involving data distribution in non-production environments or in the cloud.

Here are 4 best practices for data masking to help build a secure data pipeline for your enterprise.

Minimize unmasked data 

The vast majority of sensitive data that resides in companies is not sitting in production, it’s in the lower environments. Companies have to minimize the footprint and enhance their security posture by masking enterprise data and replicating only the transformed set for end-user access. 

The less masked data your company has, the less there is for bad actors to steal your most valuable asset. 

Accelerate masked development and testing

Nobody likes to wait. Giving developers the right capabilities to data sets will empower them to get the job done efficiently and effectively.

From there, once masked data is moved into a cloud environment, developers and testers can refresh, bookmark, rewind and integrate data using self-service controls. 

Enable offsite or hybrid cloud operations

Masked data must be transferred to cloud environments, such as AWS. Similarly, replicating the masked data to a remote data center will allow the team for offshore development and testing as well as outsourced analytics. 

Empower reporting capabilities

Powerful analytics will allow business analysts to easily spin up or spin down point-in-time virtual copies or production data, further helping the team make valuable business decisions. That way, your end users have access to the most up-to-date, masked information without comprising regulatory requirements. 

Learn more about how you can safeguard confidential data through data masking. The Delphix Dynamic Data Platform provides an enterprise-wide approach to data masking and data virtualization capabilities that can help sync, mark and deliver your data securely and rapidly.