9 Best Practices for PII Masking

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. 

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).   

How PII is Masked 

A variety of 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.  

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.  

9 Best Practices for PII Data Masking 

When enacting a plan for PII masking, it’s important to follow some guidelines 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. 

Planning Best Practices

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 data privacy compliance. But having a plan in place ensures that your organization properly seizes that opportunity.  

Keep the following practices in mind when you’re building your PII masking plan. 

1. Create a Thorough Masking Plan 

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

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Data Discovery Best Practices

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. Here are some best practices for tackling it.  

3. Define the Requirements for Protecting Your Sensitive Data 

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. 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. 

4. Enact Policy-based Data Discovery Wherever Possible 

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. 

Process Best Practices

Building sustainable and replicable processes is a key component of any DevOps initiative. The following best practices concern ideal processes for masking.  

5. 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.  

6. 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.  

7. 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. 

Execution Best Practices

The best static data masking solutions are automated processes. Automation ensures a seamless workflow. Nevertheless, there are a few key best practices to keep in mind during this stage: 

8. Avoid Unnecessary Masking 

Masking data can get expensive. So, it’s important to only mask PII in a way that’s necessary for QA testing and development scenarios. 

Common examples of unnecessary data masking include: 

• Masking massive history tables not actually needed in lower environments. 

• Masking internal identifiers found in primary and foreign keys. 

• Employing needlessly complex transformations on comment or text files that aren’t required for testing purposes. 

9. Incentivize Masking Compliance by Enabling Fresher and Faster Data 

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.  

2024 Masking Insights: Revealed and Analyzed by the Delphix Experts

How are you protecting sensitive data in non-production environments? In our recent State of Data Compliance and Security Report, 66% cited use of static data masking. Discover other masking insights, including how to use masking for data compliance — without making trade-offs for quality or speed!

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The Delphix Difference: Effortless, Customizable PII Data Masking Plus Virtualization 

Delphix gives organizations extensive and efficient data masking capabilities to protect them from regulatory and security risks.  

Delphix helps with planning and data discovery by detecting PII across production and non-production environments, regardless of location or type. Delphix’s library of APIs allows for seamless policy-based data discovery, iterative implementation, and other customizable actions. 

Another key function of the Delphix DevOps Data Platform is data virtualization. Data virtualization lets DevOps teams effortlessly refresh data as it is loaded. This gives developers and testers an added incentive to mask data. 

Using Delphix ensures that data is irreversibly masked such that it is changed to fictitious data values that maintain referential integrity. That way, your organization can perfectly balance agility with utility in its masking processes. Discover more >> What Is Delphix?

Our team of masking experts is here to help you. With Delphix, you can build your ideal data masking campaign and carry it out flawlessly.

Request your demo today to explore Delphix PII data masking, compliance, and beyond.