Ensure PCI Compliance and Protect Cardholder Data

Automatically discover and secure sensitive data subject to PCI-DSS.

Hidden Risks for Payment Information

A data breach or compliance violation for customer payment information can cripple an organization, with these incidents becoming financially costly and damaging to a company’s reputation. Non-production data environments used for development, testing, and analytics often contain most of the data subject to PCI-DSS—representing a huge hidden risk.

Delphix Automatically Profiles and Masks Sensitive in Non-Production Environments

Delphix takes a comprehensive and API-driven approach to masking sensitive payment data in non-production environments. Delphix eliminates privacy risks while ensuring data is still usable for developers, testers, and analysts.

Data compliance begins with profiling

The Delphix platform scans data values and metadata to pinpoint the location of confidential information.

Transform sensitive data into fictitious, yet realistic values

Automatically replace sensitive data values, such as credit card information, with fictitious, yet realistic equivalents that are still usable for development, testing, or reporting.

Deploy masking policies from a central solution

Configure once and centrally manage policies that can be applied consistently across the enterprise. A robust API set also allows teams to integrate Delphix with other security solutions to protect against data loss.

Deliver compliant data in minutes

Delphix combines data masking with data delivery, enabling users to provision lightweight copies of data to developers, testers, or business analysts—in just minutes.

Key Benefits

Operationalize in days, not months or years
Deploy quickly with preconfigured discovery templates and algorithms specifically tuned for PCI-DSS.
Deliver compliant data anywhere
Automate the delivery of compliant data on-premises and across the multicloud, in just minutes.
Mask any data source
Mask all of your enterprise data sources while retaining realism and referential integrity.