Today, the greatest risks to an enterprise’s data and intellectual property typically come from inside the organization. As workers and applications become more distributed, an organization’s most important secrets are constantly being put at risk by the everyday mistakes of busy employees as well as a variety of malicious insiders.
As enterprises and their risks continue to evolve, how must their requirements for mitigating insider risk also evolve?
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In this paper, you will learn:
Limitations of Data Loss Prevention (DLP) and Behavioral Tools for Insider Risk
Data Loss Prevention (DLP) tools can analyze data but are limited to content that fits a standard pattern such as credit cards and Social Security Numbers, which makes it almost impossible to classify intellectual property. More recently, behavioral analytics tools have been deployed to identify unusual or suspicious behavior. However, such tools typically have no idea what the data actually is, don’t block, and often require extensive manual investigation.
How to Build a Better Insider Risk Management Program with Data Detection and Response
Data Detection and Response (DDR) platforms introduce a new approach that lets security teams see and control virtually any type of data based on its risk to the business. These technologies give security teams full visibility into the entire lifecycle of of any enterprise data, including the user and application that created it through the many times it was shared, copied, or modified.
Top Requirements for Modern Insider Risk Management
What features do modern enterprises need in an insider risk management solution? Get our guide on evaluating modern insider risk management tools.
