And with a growing number of breaches originating on mobile devices according to Verizon’s Mobile Security Index 2020, combined with 83% of all social media visits in the United States are on mobile devices according to Merkle’s Digital Marketing Report Q4 2019, applying machine learning to harden mobile threat defense deserves to be on any CISOs’ priority list today.
Using supervised machine learning algorithms that factor in device detection, location, user behavior patterns and more to anticipate and thwart phishing attacks is what’s needed today.
The good news is machine learning algorithms can thwart hacking attempts that get in the way making mobile devise employees’ IDs, streamlining system access to the resources they need to get work done while staying secure.
Keeping enterprise-wide cybersecurity efforts focused takes more than after-the-fact analytics and metrics; what’s needed is look-ahead predictive modeling-based machine learning data captured at the device endpoint.
Capturing data at the device level in real-time and using it to train algorithms, combined with phishing URL lookup, and Zero Sign-On (ZSO) and a designed-in Zero Trust approach to security are essential for thwarting the increasingly sophisticated breach attempts happening today.
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