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Digital transformation has fundamentally changed how businesses interact with their partners, supply chains, and customers. It has also exponentially increased the amount of data generated and stored by organizations.
Our data conundrum
Modern enterprises generally have hundreds of terabytes, if not petabytes, of data, much of which is unstructured. This type of data can make up 80 to 90% of an enterprise’s entire data footprint, and because it is unstructured, it is largely ignored. However, certain elements of unstructured data contain sensitive information that may fall prey to breaches.
The conundrum: We don’t know which data is sensitive; it’s like trying to find a needle in a haystack.
New tools may replace cumbersome data governance methods
With an abundance of data accumulated over many years, queries from regulators and discovery orders from legal authorities sprout up frequently.
A typical reaction by data managers may be to put an immediate process in place — perhaps having employees sign a statement vowing not to store sensitive data and then conducting training about personally identifiable information (PII). But this is a mere “Band-Aid” solution placed on the process as they hope for the best.
Alternatively, data managers can sift through mounds of data. They scan each and every document, trying to unveil sensitive data. But scanning the petabytes of unstructured data would take years. It is also quite costly and too time-consuming to achieve desired results, which causes many data managers to eschew this approach.
Sensitive data and the rise of AI-based data segmentation
An effective and efficient techn …