7 Best Practices for PII Masking That Ensure Compliance and Deliver Data Fast

Protecting PII is no longer optional as regulations like GDPR, CCPA, and the EU AI Act raise the stakes for data security. PII data masking helps organizations reduce risk by safeguarding sensitive data while keeping it usable for non‑production environments. From planning to PII data discovery to execution, get the expert-led best practices you need for fast, masked PII data today.

What is PII? 

PII is any sensitive information or data that could directly identify the person to whom it belongs. This can include names, street or mailing addresses, government-issued identification numbers, email addresses, and telephone numbers. 

Data privacy regulations across the world often require organizations to take extra precautions when handling PII. These regulations include the European Union’s General Data Protection Regulation (GDPR), the United States’ Health Insurance Portability and Accountability Act (HIPAA) for healthcare organizations, and California’s California Consumer Privacy Act (CCPA). 

However, PII often lives across databases, applications, and environments. PII data discovery enables teams to systematically find sensitive data so it can be governed, masked, and protected.

Non-production environments are singled out as the highest-risk area for PII exposure. ISO states that "nowhere is this more crucial than in software development and testing," warning that developers often need real names, social security numbers, and medical histories to ensure systems run smoothly — but "without proper protection, that data is wide open to risk". 

What is PII Masking?

Masking personally identifiable information (PII) is a highly efficient and useful way to ensure data security and data privacy compliance across an enterprise.  

But static data masking is also a detailed, nuanced process. To ensure successful PII masking, your organization must consider many factors. Some of these factors include your data inventory, available resources, and data goals. Read on to learn nine key best practices for PII data masking. 

How and When to Mask PII Data

A variety of data masking techniques can protect PII and bring it into compliance with data privacy regulations. Some of these include encryption, dynamic data masking and anonymization. But static data masking (referred to hereafter as data masking) is the most thorough and safest method for ensuring PII security and compliance of data for application developers and testers.

Data masking protects sensitive data by irreversibly replacing original, real data values with fictitious but realistic equivalents. Masked data maintains referential integrity — it’s as useful for DevOps professionals as the original data values.  

Protecting data fits into two broad categories: protecting data in production environments and protecting data in non-production environments. To clarify, development, testing, and staging tasks are carried out in non-production environments. In contrast, production environments are the portion of software that most end users interact with and/or see. 

Protecting data in each of these environments has different considerations — which will dictate what data needs masking. In non-production environments, developers and testers never need to see unmasked data. Whereas in production environments, certain users may need to view unmasked sensitive data at certain times. So, masking needs are very different for production environments. 

How PII Data Masking Helps Mitigate Data Security Risk

According to our Perforce Delphix 2025 State of Data Compliance and Security Report, 95% of surveyed organizations reported storing more sensitive data in non-production environments. This data is now at greater risk, due to sensitive data sprawl. When PII is shared across multiple systems, your company and that data are more likely to fall victim to:

• Unauthorized access
• Data breaches
• Data privacy law violations

In fact, 60% of organizations have experienced data breaches or theft in non-production environments, an 11% increase from last year. But with the help of PII data masking, your organization could limit the spread of sensitive data and its associated risk. When that PII is replaced with fictitious but realistic data, you no longer have to worry about people's real data getting into the wrong hands.

7 Best Practices for Your PII Data Masking Strategy

It’s important to follow some guidelines while masking PII, to ensure optimal results. The following best practices, grouped by the various components of masking they entail, will ensure comprehensive data security and data privacy compliance across your enterprise. 

1. Create a Thorough PII Masking Plan 

Don’t make a half-hearted effort to build a PII masking plan. 

Thomas Edison said it best: “Good fortune is what happens when opportunity meets with planning.” PII masking is a great opportunity for your organization to guarantee data security and eliminate data privacy risks in non-production environments. But having a plan in place ensures that your organization properly seizes that opportunity.  

PII masking requires dedicated resources and dedicated infrastructure to be successful. Set aside teams and a budget to carry it out. 

PII masking also requires a realistic, detailed project plan. Setting arbitrary dates for masked data delivery and imprecise resourcing will fail. Instead, chart out a realistic level of effort in your plan. You can infer this based on your existing resources and the amount of data you have to mask.  

2. Plan to Iterate and Triage Masking 

Working with data is messy. PII masking is also a specialized development project. So, attempting to mask a complete set of applications — including every piece of sensitive data — at once is a risky move.  

Successful masking projects are iterative. And teams should always identify and mask the most dangerous or sensitive information first — like a medical professional triaging patients.  

Assume that it will take multiple passes before your PII masking jobs produce satisfactory results. Design your masking program with this assumption, and you’ll set yourself up for success. 

3. Enact Policy-Based PII Data Discovery Wherever Possible 

Before masking starts, it’s essential to understand the locations and classifications of your sensitive data/PII. The process of gathering this information is known as data discovery. 

A data discovery process should be possible for any data source in your enterprise. So, it’s key to be thorough with data discovery.  

But data management is subject to constant changes. Data privacy regulations, your organization’s applications, and your organization’s environments will change. So, you should be able to version-control your sensitive data inventory as these changes occur. And you should build these policy expectations into your data discovery processes. 

Want to see how easy sensitive data discovery is with Perforce Delphix? Watch this quick demo from our colleague Felipe Casali and see it for yourself. 

4. Create a Process that Balances Protection with Utility 

It’s easy to mask all data in non-production environments. But it’s hard to mask data while preserving the utility of the data for testing, development, and analytics purposes.  

Your organization may need to make trade-off decisions when building your masking processes. Some key decision points will center on the rules and processes for data de-identification. Others will concern how much time and effort to invest in “preserving” the development and testing value of the data.  

So, build a process that balances protection and utility. And be sure it aligns with all departments that are involved with masking.  

5. Use Resilient, Nimble Processes to Combat Analysis Paralysis  

During a PII masking campaign, too many organizations do nothing out of fear of “breaking” systems. They may get sidelined by analysis paralysis and wait for teams to sign off on masked data when nobody has the time or motivation to do so.  

Avoid this by building resilience and agility into your PII masking processes. These traits will help your teams move fast and recover from mistakes fast. Using a process that can be tweaked and optimized over time will help teams implement data masking faster.  

6. Future Proof Your Data Risk 

Protecting PII in non-production environments takes time. So, it’s important to make sure that you can reduce as much data risk moving forward as possible.  

Future proof your data risk by integrating data masking APIs into existing data processes. This gives your organization a fully integrated masking solution. Be sure to post information in a strategic location about the data that’s protected. This will give you traceable data protection. 

7. Incentivize Masking Compliance by Enabling Fresher and Faster Data 

The best static data masking solutions are automated processes. Automation ensures a seamless workflow. Nevertheless, keep in mind this best practice while in the execution stage.

Masking data can feel like a one-sided project, as development teams and testing teams themselves don’t get any benefits from masked data. But if your organization gives these teams greater flexibility and agility, you give them an incentive to adopt masking. 

One of the best values a development team or testing team can get is the ability to refresh, rewind, and self-service their databases. Adopting a data virtualization solution alongside a masking solution will give teams these capabilities. Doing so incentivizes them to adopt masking because it gives them fresher data and greater control of it, too.  

The value-add of data virtualization working in tandem with data masking ensures that your technology leaders won’t have to tell teams, “you have to use masked data moving forward.” Instead, they can tell teams why using this masked data will benefit them, too.  

Real-World Examples of Masking PII

IDC Study*

Organizations that mask PII ensure compliance and they move faster. A recent IDC study of Delphix customers found that teams using Delphix:

• Achieved 58% faster application development.
• Protected 77.2% more environments.
• Realized a 408% ROI with payback in under six months.

*Source: "IDC Business Value White Paper, sponsored by Delphix, by Perforce, The Business Value of Delphix, #US52560824, December 2024"

How UniSuper Secures Sensitive Data

UniSuper needed to protect sensitive client PII. By using Delphix, they masked PII data, reduced manual masking errors, and gained a 70% improvement in development efficiency. There's no need for them to trade-off security for speed — or vice versa.

How Molina Healthcare Ensured HIPPA-Compliant PII

Molina Healthcare needs to mask highly sensitive PII / PHI for compliance with HIPAA. By using Delphix, they get HIPAA-compliant data fast AND provision non-production databases in under 10 minutes. They also save $6-10M in storage by virtualizing that data. 

How 11 More Enterprises Mask PII Fast

Need more examples? Download the customer booklet Protect Sensitive Data and Ensure Compliance with Data Privacy Regulations to get examples from 11 enterprises, including SelectHealth, Delta Dental, and ANZ Bank. Follow the examples of these enterprise leaders to mask PII data for data privacy compliance in your lower environments today.