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Data Masking with the DevOps Data Platform
- How Delphix Discovers Sensitive Data
- Securing Sensitive Data: How it Works with Delphix
- Deep Dive on Delphix’s Masking Algorithms Frameworks
- Frameworks How to Execute Masking Jobs in Delphix
- How Delphix Scales for and Integrates with Enterprise Workflows
- Get Enterprise Data Masking Built for Scale, Speed, and Trust
White paper > Data Masking with the DevOps Data Platform
Securing Sensitive Data: How it Works with Delphix
Delphix secures sensitive data across non-production systems using static data masking. Delphix masking algorithms create a structurally similar but fictitious version of data that can be used for purposes such as application development and testing. Masking protects the actual sensitive information while generating a functional substitute for occasions when the real data is not required.
Here is what makes Delphix masking uniquely robust compared to other popular masking options:
Delphix Uses Static Masking
Static data masking replaces sensitive values with fictitious, realistic equivalents directly at the data source. Delphix uses static masking to alter the data permanently before anyone copy it to less secure non-production databases.
This approach delivers robust protection because it leaves no path back to the original information. If a breach occurs in a non-production environment, attackers only find useless, fictitious values.
By masking personally identifiable information and protected health information proactively, organizations achieve compliance with zero trust security models.
Delphix Uses Deterministic Masking
Deterministic masking generates realistic synthetic data while using production data as a reference to ensure consistency across systems. Delphix applies deterministic algorithms to guarantee that the same input always produces the exact same masked output in every environment.
For instance, if a specific customer name transforms into a fictitious name in one database, it transforms into that exact same fictitious name across all integrated applications.
This consistency prevents application failures and eliminates wasted troubleshooting time during complex testing cycles. Security teams can protect sensitive data without breaking the critical logic that development teams need for innovation.
The determinism is repeatable and consistent across data types and data sources
E.g. A avalue is changed the same way irrespective of where it is stored, when it is processeed, how many times it may be processed and in any way it is processed ( UI, CLI, API)
Delphix Masking Is Irreversible
Data security demands irreversibility to prevent malicious actors from reconstructing sensitive information. Delphix ensures masking is irreversible, meaning that masked data cannot be reverse-engineered or restored to its original state. Even privileged users with deep system access cannot uncover the real values once the masking process finishes.
This permanent transformation mitigates regulatory exposure and simplifies audit processes for compliance teams. Organizations can confidently distribute masked datasets globally without fearing data exposure or privacy violations.
Delphix Masking Preserves Referential Integrity
Modern enterprise data estates feature complex relationships spread across multiple applications, pipelines, and clouds. Delphix preserves referential integrity by consistently applying deterministic masking rules across these heterogeneous data sources. When the platform scrambles a primary key, it masks all linked instances of that key identically throughout the entire database ecosystem.
This unified approach prevents the disconnected data silos that typically cause integration tests to fail. Developers receive highly reliable test data, and organizations avoid the costly delays associated with broken data relationships.
Delphix Masking Maintains Data Realism
To build and test applications successfully, engineering teams rely on data that accurately reflects production environments. Delphix maintains data realism by outputting masked values that retain the structural characteristics and formats of the original data. The platform preserves geographic distributions, payment card formats, and human-readable names without compromising security.
This realistic data ensures that performance testing, functional testing, and analytics workflows remain highly accurate. Teams can innovate rapidly with secure data that behaves exactly like the real information they depend on.
SOLUTION DEMO
Masking Data to Mitigate Compliance Risk
In this demo, my colleague Bruce Liu explains how Delphix identifies sensitive data, applies automated masking, and preserves referential integrity to keep applications functioning with realistic data.