Frost & Sullivan’s Independent Analysis - 2016 Machine Learning-based Mobile Threat Detection Study

Today’s workforce increasingly accesses sensitive data from outside secure corporate networks using their own devices. Bring-your-own-device (BYOD) strategies and policies offer great benefits but are difficult to manage since users don’t want their private data monitored.

Conventional mobile device security solutions usually focus on protecting enterprise networks from threats by using predefined parameters through signature-based technology. However, this is not effective against new unknown threats or for identifying frequent changes in device parameters.

“Frost & Sullivan’s independent analysis clearly shows that by virtue of its ability to integrate a best-in-class behavioral analytics engine based on machine learning algorithms into a mobile threat detection management solution, the z9 technology has obtained a huge advantage over its competitors.”

Award-Winning Mobile Threat Detection Technology

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Technology Attributes
  • Comparison of multiple solutions
  • Implementation strategies
  • Current and future business value

Results of the Study Show

Frost & Sullivan report results

About Frost & Sullivan

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