Applying Machine Learning To Identify Compromised Credentials

By David Rosenberg, CTO-DB Networks It’s not uncommon for large enterprises to have hundreds or perhaps hundreds of thousands of databases — many out of date, many no longer used, and the vast majority not monitored or properly secured from possible attacks. Unauthorized databases access is increasingly a result of credential theft, and IT personnel are urgently trying to get their arms around the situation. They know they need to not only discover all their own databases, but must figure out how to secure them once they do. An Osterman Research study found nearly 40 percent of enterprises are unable to monitor the majority of their databases in real time. When asked what database security issues are of most concern, compromised credentials was the top concern of the survey respondents.…

Link to Full Article: Applying Machine Learning To Identify Compromised Credentials

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