Enterprise data masking is non-negotiable.
With data breach incidents regularly making news headlines and increased pressure from privacy regulations, such as GDPR, CCPA, PCI DSS, CPRA and HIPAA, organizations must ensure that sensitive data is secure across their enterprise.
What Is Enterprise Data Masking?
Enterprise data masking, also referred to as data de-identification or obfuscation, is a method of protecting sensitive data by replacing the original value with a fictitious but realistic equivalent.
Masked enterprise data is essential for enabling innovation. As IT leaders realize that data is key to building data-driven applications and unlocking competitive advantage, it’s becoming increasingly important to provide secure access to data that flows across an organization to innovate faster and at scale, without compromising privacy and security.
The vast majority of sensitive data in an enterprise exists in non-production environments used for development and testing. Non-production environments represent the largest surface area of risk in an enterprise, where there are up to 12 copies for non-production purposes for every copy of production data that exists. To test adequately, realistic data is essential, but real data runs considerable data security risks.
Companies need to be able to address these challenges by combining modern security practices, like data masking, with the ability to move data fast to those who need it, when they need it.
6 Key Components of Enterprise Data Masking
There are 6 key components your data masking solution must have to ensure both security and innovation across your enterprise.
Referential Integrity
Application development teams require fresh, full copies of the production database for their testing. True data masking techniques transform confidential information and preserve the integrity of the data.
For example, George must always be masked to Elliot or a given social security number (SSN) must always be masked to the same SSN. This helps preserve primary and foreign keys in a database needed to evaluate, manipulate and integrate the datasets, along with the relationships within a given data environment as well as across multiple, heterogeneous datasets (e.g., preserving referential integrity when you mask data in an Oracle Database and mask data in SQL Server database).
Realistic
Your data masking solution must give you the ability to generate realistic, but fictitious, business-specific data, so testing is feasible but provides zero value to thieves and hackers. The resulting masked values should usable for non-production use cases. You can’t simply mask names into a random string of characters.
Irreversibility
The algorithms must be designed such that once data has been masked, you can’t back out the original values or reverse engineer the data.
Extensibility & flexibility
Your data masking solution needs to work with the wide variety of data sources that businesses depend on and should be customizable. Moreover, businesses should not be required to program their own masking algorithms or rely on extensive administrator involved.
Repeatable
Lastly, masking is not a one-time process, it should happen repeatedly as data changes over time. It needs to be fast and automatic while allowing integration with your workflows, such as SDLC or DevOps processes.
Is Your Data Masking Fit for the Enterprise?
Not all data compliance solutions are created equal. For enterprise-grade requirements, your data masking strategy must go beyond just basic anonymization. It needs to scale and adapt to the complex and dynamic nature of enterprise systems, especially as data environments grow in size and complexity.
Here’s how to evaluate whether your data masking approach is fit for your enterprise:
- Scalability: Can your solution handle terabytes or petabytes of data across hundreds of environments? Enterprise systems often deal with large-scale data, meaning your masking solution should scale effortlessly.
- Cross-environment capability: Enterprises often operate hybrid environments that include on-premises systems, cloud platforms, and legacy databases. Your masking tool must be compatible across these disparate environments to ensure seamless integration.
- Compliance assurance: Does your solution meet regulatory requirements like GDPR, HIPAA, or PCI DSS? If not, your enterprise could face stiff penalties for non-compliance.
- Speed and automation: Masking should not hamper innovation. Your solution should automate repetitive processes and rapidly deliver compliant data, integrating seamlessly into DevOps or CI/CD pipelines.
- Support for complex testing: Enterprise apps often require complex testing scenarios that rely on referential integrity and realistic data. A robust masking solution should allow these capabilities while maintaining data security.
If your current solution falls short in any of these areas, it may be time to re-evaluate your approach. Without an enterprise-ready data masking strategy, you risk inefficiencies, compliance issues, and data breaches.
Mask Enterprise Data More Effectively
Learn how to identify, mask, and protect sensitive information. Explore best practices for data masking with practical insights straight from Perforce Delphix.
Enterprise Data Masking Examples
To emphasize the importance and transformative potential of enterprise data masking, here are real-world case studies where organizations leveraged Perforce Delphix masking solutions to address challenges, reduce risk, and drive efficiency.
Related blog >> What Is Delphix?
1. Proximus (Telecom)
Proximus faced significant hurdles maintaining a 24/7 operation while ensuring uninterrupted testing for their teams. Their legacy process of refreshing production databases was time-consuming, often taking up to two weeks and leaving testers idle.
Solution:
With Delphix, Proximus refreshed 60 applications in just one weekend. The enterprise reduced data masking time by 97% and non-production storage costs by 85%, resulting in €7 million in savings over three years.
2. California State University (Education)
With growing data demands, rigorous security mandates, and the pressure to reduce operational costs, California State University needed a solution that could perform at scale.
Solution:
By integrating Delphix's data masking, the institution executed 2,000 data requests per month in minutes and achieved $2 million in development storage cost savings.
3. Delta Dental (Healthcare)
Data extraction and masking processes at Delta Dental took up to eight weeks, delaying innovation and leaving sensitive data exposed in non-compliant states.
Solution:
With Delphix, data for testing and development could be masked and delivered within minutes to over 200 developers. This significantly reduced storage costs and improved compliance while accelerating development cycles.
4. Worldpay (Finance)
Worldpay faced immense challenges managing sensitive customer data while ensuring test environments were equipped with the correct data. Their process relied on outdated data for testing and lacked automation.
Solution:
By integrating Delphix, Worldpay achieved 7x faster environment refreshes and reduced test data storage by 75-80%. They automated end-to-end processes and ensured compliance without delays.
5. Boeing Employees Credit Union (Financial Services)
When handling vast amounts of sensitive data, consistency in masking and support for diverse databases were critical challenges for Boeing Employees Credit Union.
Solution:
With Delphix, the credit union masked 680 million data rows in just 15 hours. Over 200 developers could now access masked data on-demand, supported by extensive masking templates and automation.
These examples demonstrate how enterprise data masking drives efficiency, compliance, and innovation across industries.
Delphix Data Masking Capabilities
Comply with Privacy Laws and Protect Against Breach
Over half of companies have experienced data breaches in non-production environments. With Delphix, you can centralize and implement masking policies enterprise-wide, ensuring compliance with key regulations like GDPR, CCPA, HIPAA, and PCI DSS. Masked data eliminates the risks associated with handling sensitive information in less secure environments.
Key Benefits:
- Detect and automatically mask sensitive data like names, emails, and payment information.
- Prevent regulatory penalties with thorough compliance measures.
- Use out-of-the-box algorithms to simplify implementation and boost reliability.
Integrate Data Masking with Data Delivery
To accelerate innovation, the Delphix DevOps Data Platform pairs comprehensive masking capabilities with unparalleled data delivery. Masked, virtual copies of data can be created and delivered to developers, QA teams, or analytics pipelines in minutes, eliminating delays common in traditional processes.
Key Benefits:
- Automated delivery of compliant, secure data.
- Supports hybrid environments from on-premises systems to cloud-based infrastructure.
- Reduces storage costs while delivering full production-like datasets.
Speed and Agility at Scale with DevOps Integration
Innovation happens fast, and your systems need to keep up. Delphix integrates seamlessly into DevOps and CI/CD pipelines, providing masked and compliant data directly into workflows.
Key Benefits:
- Continuous masking as data evolves over time ensures ongoing compliance.
- Enables faster software releases by removing bottlenecks in the data pipeline.
- Empowers teams to release high-quality software faster, with fewer delays.
Take the Next Step with Delphix
Transform the way your enterprise handles sensitive data with Delphix data masking solutions. By combining compliance, automation, and scalability with the ability to deliver masked data on demand, Delphix ensures you never have to choose between security and innovation.
Want to see it in action? Request a demo today to discover how Delphix can eliminate compliance risks, accelerate software delivery, and reduce costs for your enterprise.