Imposter fraud is the second-most common type of fraud reported to the Federal Trade Commission, with around one-fifth of all cases resulting in financial loss to the victim. This often occurs because of a failure on the part of organizations to protect personally identifiable information (PII).

Fraud is only one type of attack that may occur. Phishing is another exceptionally common data security threat. It often results from crawlers collecting email addresses, one type of PII, on the open web. 

One method of preventing sensitive personal data from falling into the hands of hackers and would-be imposters is PII masking. This involves removing or replacing aspects of sensitive data that one might trace back to particular people. Masking reduces the risk of imposter fraud and other security breaches.

So, aside from this general definition, what is PII masking and how can you use it to protect your clients and employees? Here's an in-depth look with additional tips on how keeps your data safe.

Table of Contents

  1. Types of PII Data
  2. Data Governance Safety Policies
  3. Types of Data Masking
  4. Protecting Clients with Data Masking/Obfuscation
  5. Data Protection with

Types of PII Data

What is PII? It includes things like email addresses, social security numbers, phone numbers, credit card numbers, and employee identification numbers, along with names and addresses. Anything that can identify you or access your personal information is PII. 

This data may be collected through various means like surveys, application log files, and many others. Most companies and services have a public privacy policy. In that policy, they express their compliance with various security frameworks and laws, inform users what personal data they may use and how, and promise a certain level of security or anonymity for any data users may offer. 

When companies are unable to fulfill the obligations set forth in these policies, it often results in a serious breach and possibly lawsuits. Although few consumers read these policies before agreeing to them (less than 10% according to Pew Research,) they are extremely important for understanding security measures or frameworks a company may put in place to protect personal information.

Data Governance Safety Policies

Some of the most common frameworks for protecting PII include HIPAA, GDPR, CCPA, and PCI DSS. 

  • HIPAA: Health Insurance Portability and Accountability Act. This federal statute was passed in 1996 to protect sensitive information relating to healthcare. It also regulates the digital transfer of such data, ensuring client safety. Many major data management and BI (business intelligence) platforms like Oracle, Microsoft Power BI, and advertise the capacity of their tools to help clients ensure HIPAA compliance. 
  • GDPR: General Data Protection Regulation. GDPR is an EU statute ensuring data privacy both within the EU and for data transferred outside the EU. It gives individuals a certain amount of authority over the use of their data and also regulates its protection to help avoid data breaches. GDPR helps avoid unauthorized exposure of personal information.
  • CCPA: California Consumer Privacy Act. CCPA is a benchmark policy that gives clients protected by California law additional data privacy rights and protections. 
  • PCI DSS: Payment Card Industry Data Security Standard. PCI DSS is a set of standards and regulations used to protect cardholders from financial fraud. 

These policies and laws are one reason that companies have to take measures to protect data through data masking.

Types of Data Masking

Data masking, also called obfuscation or pseudonymization, effectively protects personal data by hiding it from public view. It restricts data access to particular organization team members to specific times. There are two primary types of masking: static and dynamic.


Static masking relies on database replication to preserve the original data. The replicated data is then masked for sharing with third parties as needed. Since only masked data is shared, PII remains secure and risk is minimal. 

Static masking has certain limitations: mainly that the entire database has to be replicated, requiring large amounts of time and resources. Also, you will need to have enough storage space for the entire replicated database. Replication has to be performed regularly so that the replica reflects any changes to the original database. 


Dynamic data masking masks data in real-time. This means that every user who accesses the database sees only masked data and no exposed personal data. However, the original data remains intact. This saves processing time since there is no need to replicate the entire database and eliminates the need for large amounts of storage space. 

Data managers also call this on-the-fly masking. It involves the use of a reverse proxy for maximum efficiency. Since systems can perform dynamic masking during the ETL (extract, transfer, and load) process instead of afterward, it provides the greatest level of security. However, managing a reverse proxy can still be time-consuming. 

Both static and dynamic masking have different use cases depending on the intended audience, the resources of your company, and the security requirements for your particular industry.

Protecting Clients with Data Masking/Obfuscation

There are various methods of data masking utilized by data management platforms. These include encryption, substitution, shuffling, nulling, and scrambling. 


Companies use encryption algorithms to protect data in databases or during transfer. This is the most secure method because an encrypted dataset protects all the data without discrimination. No one can view it without the key. Most data management systems offer field-level encryption for this purpose. uses highly secure encryption to protect sensitive data. 


Substitution replaces the original data with fake data that has a similar appearance and the same format but protects personal information. This can still be used as test data for application testing and various other purposes. 


Shuffling is similar to substitution but not quite as thorough. It replaces the original data randomly, but only with other data elements in the same column. 


Data management platforms primarily use scrambling for numerical data. It is simply the process of scrambling numbers or other characters around within a dataset. For instance, individual numbers within a numerical piece of data, whether it be an address or any other numerical format, can be rearranged to conceal the particular piece of information. 


Companies sometimes use nulling for the most sensitive information. Instead of replacing real data with fake data, it replaces it with a null value for everyone except the intended user. 

Data managers can use any of these methods to mask data or produce test data without compromising PII. Different situations may call for different methods, but the principle remains: keep PII secure.

Data Protection with offers data protection at its best to companies in a great variety of industries. With, companies can easily develop secure pipelines to and from various sources and ensure the safety and privacy of their client's data. performs dynamic data masking, allowing you to maintain security even during the transfer of data or as several users access the database. simplifies data integration and security. This allows you to focus on what you do best: serving your clients in a way that will grow your business and foster trust. With native connectors to over 100 different applications and data stores, we help businesses manage data securely and at a remarkably low cost in time and resources.

To discover more about's offerings and services, contact us today and build a pipeline with a free trial.