Page 132 - Cyber Defense eMagazine RSAC Special Edition 2025

solutions with deep learning capabilities can see a threat it wasn’t trained specifically to identify and,
employing pattern recognition that’s more advanced than traditional AI and machine learning, block the
attack. Leading tools have an efficacy greater than 99%, giving IT teams more power to stop zero-day
attacks than ever before.

Augment detection and response, reduce overall risk

Using advanced platforms alongside traditional tools, it’s now possible to achieve two critical goals:
stopping more zero-day attacks and reducing security operations center (SOC) stress. With advanced AI
on the frontlines, detection and response solutions no longer need to wade through every potential threat
in the search for genuinely malicious items. Existing platforms can be more efficient and accurate. In
addition, as more incoming dangers are evaluated and managed automatically, the SOC team can focus
on high-priority alerts elevated by the cybersecurity tools rather than wading through the entire flood of
concerns.

New solutions with AI and deep learning at their core have a high success rate, but they aren’t perfect.
Companies should continue to employ a layered approach to cybersecurity, with deep-learning platforms
and traditional detection and response tools working hand in hand to thwart zero-day, ransomware, and
other attacks. Together, these layers can block threats at multiple levels, relieve the SOC team of
unnecessary alerts, and drastically reduce the organization’s overall risk.

About the Author

Dave Floyd is the Vice President of Cybersecurity Sales and Service for Hughes
Network Systems. He works directly with organizations and enterprises across
industries to provide tailored cybersecurity solutions that address pain points and
build a comprehensive cybersecurity posture for their businesses. Dave can be
reached online at linkedin.com/in/davidefloyd/ and at our company website
https://www.hughes.com/

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