Security News > 2021 > September > Leveraging AI and automation to identify sensitive data at scale

In this interview with Help Net Security, Apoorv Agarwal, CEO at Text IQ, talks about the risk of unstructured data for organizations and the opportunity to leverage AI and automation to identify sensitive data at scale.

Ideally, organizations should have a handle on where sensitive information is sitting in their data.

It's impossible to have strong data governance without some level of automation; for instance, the volume of data generated by enterprises is rising exponentially and relying on humans to take stock of all the sensitive information that's laying buried in their database-undetected, and more often than not, in an unstructured format-simply does not work at scale.

Data breaches and ransomware attacks will continue to happen, but organizations have a real opportunity to leverage AI, which gives them the ability to proactively identify sensitive and personal data at scale; once the data is identified, they can choose to redact, delete, encrypt or take whatever the necessary steps are to secure it so that it never falls into the wrong hands.

Secondly, this unstructured data is replete with all types of sensitive information: trade secrets, personal information, health information, intellectual property, etc; for instance, no one builds a structured database containing an organization's trade secrets-it's more likely lying scattered in emails, chats, Excel sheets and other forms of unstructured data.

The challenge presented by unstructured data is that it is voluminous and finding the sensitive information lying within it is like looking for the proverbial needle in the haystack.

News URL

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