Data can be the lifeblood of a business or its most damaging liability. Important documents are often stored in ramshackle file systems – or worse, copied to the cloud with little to no process controls. In fact, about 40 percent of corporate data that is uploaded to the cloud uses ad hoc file sharing applications, more than any other category. As a result, timely information gets lost amid haphazard processes. Business decisions, therefore, are made on instinct, and the competition wins. Later, during the audit, it’s not just the shareholders who are upset but industry regulators, fining you for compliance failures.
But it doesn’t have to be this bleak. Clearly, with sophisticated data tools, intelligent storage systems and artificial intelligence, there is no reason to mismanage data.
If the Cambridge Analytica scandal was not enough of a wake-up call, data privacy regulations — such as GDPR and CCPA — ensure that companies manage their information securely or face serious sanctions and fines.
In this series of posts, I will demonstrate how to map and secure sensitive data with the help of artificial intelligence. I will explain how to specify several types of sensitive information plus provide concrete methods to detect their location in organizational data.
Adam is a lead data who joined NetApp following the Cognigo Acquisition. Adam specializes in applying machine learning and natural-language processing solutions to help organizations map and protect personal and sensitive data. Adam holds M.Sc. in Mathematics.