Enterprise Data Privacy and Its Challenges

In 2022, an estimated 97 zettabytes of data were created and consumed across the world. Not all of that data is stored, but a significant portion of the data that is stored and processed is considered Personally Identifiable Information (PII) or Protected Health Information (PHI) by the patchwork of ever-evolving data privacy regulations U.S. businesses must comply with. It’s no surprise that data privacy, protection, and security are top priorities for compliance and security executives.

What is data privacy?

Data privacy refers to the proper handling of sensitive data like PII and PHI to meet regulatory requirements and protect data confidentiality. There are numerous regulatory frameworks businesses may have to comply with, depending on the nature of the data they collect and process, but all require businesses to develop and implement compliant policies for the following:

Businesses that fail to implement compliant data privacy policies can expect consequences that range from fines and the loss of key vendor relationships to legal action by the state and their customers. Consumers are more aware of data privacy issues than they once were, and the reputational damage of a data loss incident may be irreversible.

The challenges of enterprise data privacy

The rapid evolution of privacy regulations may be the biggest challenge to enterprises' data privacy compliance. As data privacy laws change to account for new sensitivities and technologies, businesses must stay up-to-date with the latest regulations and adapt their data privacy practices accordingly.

But the challenges aren’t all legal; there are many technical hurdles to data privacy compliance. To comply with privacy regulations, businesses must first be able to identify PII, PHI, and other sensitive data when it enters their systems.

Data discovery, which involves inventorying and classifying data within an organization, is crucial for identifying sensitive information and determining the best ways to secure it. If data discovery focuses on what data is, data mapping focuses on where it is. Data mapping helps businesses understand the relationships between data sets and systems by identifying the locations of data and how it flows through their services and networks.

Both data discovery and data mapping are extremely challenging given the volume, velocity, and variety of data propagating around decentralized services operated by large businesses.

There are data privacy solutions on the market that promise to mitigate the compliance risk. But most focus on downstream detection of sensitive data. They detect and mitigate data risks in already-collected data.

There remains a considerable risk that stored sensitive data may already have propagated to other services or to areas with different regulatory compliance regimes, making it difficult for organizations to guarantee that their customers’ data is managed in compliance with privacy regulations.

Data privacy compliance is an unsolved problem for large businesses

Enterprise data privacy is a critical concern for businesses in today’s data-driven world. Data privacy tooling helps businesses mitigate the risks and challenges associated with privacy compliance, but existing data privacy products do not offer a complete solution for decentralized, event-driven applications.