Many large enterprises underestimate the vulnerability of their data. The potential for loss or breach is great, not only from external threats but accidental exposure by employees or partners of the company. This vulnerability exists at a time when enterprises rely heavily on their data and face greater than ever legal obligations to protect it.

One particularly sensitive subset of data is personally identifiable information (PII). Global and U.S. regulations have required companies who collect this type of data to prioritize its protection. Otherwise, they face serious fines, penalties, and loss of reputation.

Pseudonymization is one way to protect that information. Sometimes, this process is required by law. But even when it's not, it provides a functional, practical way to balance competing obligations to extract business intelligence from data, while safeguarding information held about individuals.

Table of Contents:

  1. Pseudonymization: What Does it Mean?

  2. Data Threats Faced by Large Enterprises

  3. Consequences of Data Breach

  4. How Pseudonymization Helps

  5. Mitigating the Threat

Pseudonymization: What Does it Mean?

At its most basic, pseudonymization means replacing true PII with fictional information. That way, in the event the information is released, either through planned or unplanned exposure, it cannot be connected to a real person or set of people. If desired, the same individual can have the same pseudonym across an entire data set, so the set can still function for the purpose of analytics. When done right, the data remains intact with the exception of true information that may violate an individual person's privacy.

Pseudonymizing data occurs via coding. You can set the encryption process to randomly generate fictional data at a pre-determined point. Or, you can manually mask the data at any stage, using a specific methodology for the fictional information. Some coders start with a randomly generated list of fictional data and then choose from that list to replace the identifying attributes.

Pseudonymization vs. Anonymization

Though similar in concept, pseudonymization is not the same as anonymization. The latter removes any connection between data and an individual. With anonymized data, it is not possible to tell if multiple sets of data are linked to the same person. In both cases, the original PII still exists, so it is possible to remove these masks so the true information is revealed. This is called re-identification.

Even though it is possible to link back to the original information -- and in some cases, organizations may need to do so -- encryption keys combined with external firewalls make this extraordinarily difficult to achieve without proper access and authorization.

Data Threats Faced by Large Enterprises

As much as enterprises may underestimate the vulnerability of their data, they also may make incorrect assumptions about where that threat comes from. It is not just the nefarious actors working in dark rooms to find and exploit unprotected data. In a huge number of cases, the culprit is simple human error by the organization's own employees.

A recent survey of 500 IT professionals revealed that a staggering 70 percent had experienced an accidental data breach in the previous 5 years. Half of those breaches happened in the prior year. Perhaps as a result, accidental employee breaches were among the top three concerns of the respondents, ranking as high or higher as threats from hackers and malware.

Consequences of Data Breach

Regardless of how it happens, breach of data has serious consequences, especially if the data contains PII. First, of course, is the potential damage to the data subject--in other words, the person whose private data has been exposed, either deliberately or by accident. The potential damages to an individual, such as identity theft, fraud, and other malicious activity, can affect them for years.

In addition, businesses face the loss of their reputations. Data breaches erode the trust between business and customers, who may be reluctant to hand over their information in the future. It is a public relations nightmare from which some organizations never fully recover.

Data exposure also risks information landing into the hands of a competitor. The value of data is largely in its transition into business intelligence. After a breach, someone else in the industry might have access to that intelligence. That can set the enterprise back in its process of collection, analysis, and use of actionable data.

There are also legal repercussions. U.S. companies -- depending on where and how they do business -- are usually subject to the privacy protections laid out in the GDPR (Europe), CCPA (California), HIPAA (U.S.A.) among many other laws. It is incumbent on the company to protect PII once they collect it. If they fail to do so, it can mean costly fines and penalties.

How Pseudomynization Helps

So where does pseudonymization come in? In some cases, it is actually required by specific legislation. Both the GDPR and CCPA reference pseudonymization as necessary to protect individual privacy. Therefore, it may not be just a good idea to implement this technique in enterprise data; the law may insist upon it.

More importantly, pseudonymization protects the data itself. It does not rely on the seamlessness of a structure that surrounds the data. Even if a hacker -- or error-prone partner -- happens to get past firewalls and other security protocols, they will only have access to pseudonymized data. That still ensures that PII is not revealed, even if someone gets inside the system.

Mitigating the Threat with

Here's where a data integration partner like steps in. Most enterprise-level organizations don't need convincing that pseudonymization is essential and benefits their business. But the implementation may seem daunting, in particular when data comes from various unstructured sources. The process of transformation from those sources into actionable business intelligence may be challenging enough, let alone ensuring privacy along the way. has solved this issue through our platform, which allows you to build ETL pipelines through point-and-click, drag-and-drop functionality. ETL stands for extract, transform, load, and refers to the process of converting raw data into structured, usable data. Our standards of security are second to none and we are fully compliant with privacy laws, including:

  • U.S. Health Insurance Portability and Accountability Act (HIPAA)
  • California Consumer Privacy Act (CCPA)
  • EU General Data Protection Regulation (GDPR) 

The platform offers easy, state-of-the-art encryption and decryption functionality. Encryption makes data indecipherable without security keys--and pseudonymization is partial encryption. helps you use ETL to pseudonymize and encrypt your data so you can meet your business and legal obligations. That means anyone in your organization can quickly build a pipeline, create a privacy-protected data set, and convert it all into actionable intelligence you can use to grow and build your business.

Want to know how? Contact us for a demo to learn how is right for your organization, regardless of where your data leads.