Digital Defense Introduces Frontline Threat Landscape

Insurance CIO Outlook | Tuesday, July 21, 2020

Mike Cotton, SVP, Engineering

San Antonio, TX - “ Digital Defense, Inc. today announced the release of Frontline Threat Landscape„¢, a unique feature within the companys vulnerability management technology that incorporates threat intelligence to prioritize critical vulnerabilities that can be exploited. Accessible within Frontline.Cloud„¢, the companys proprietary software as a service (SaaS) security assessment platform, the feature leverages machine-based learning to provide threat intelligence data that delivers a more granular determination of risk for vulnerabilities identified in an organizations network.

Our Frontline Threat Landscape feature is a game changer in that organizations can shed the overwhelming weight of responding to a vast number of vulnerabilities that pose minimal risk. Threat Landscape shines a light on the truly critical vulnerabilities that present the greatest risk to our clients based on their individual network characteristics, states Mike Cotton, SVP, Engineering. Some vulnerabilities may be identified as high level, but if a cybercriminal cannot exploit, spending valuable resource time to remediate is not time well spent and may ultimately expose the organization. Our technology provides the data to expose, rank and address vulnerabilities in a focused and efficient manner to significantly reduce risk

According to Gartners Market Guide for Vulnerability Assessment, Not all vulnerabilities are created equally. Exploitability, prevalence in malware and exploit kits, asset context, and active exploitation by threat actors are critical qualifiers in assessing cyber risk. The report further describes the incorporation of threat intelligence into vulnerability management technologies. Methods are applied that analyze and prioritize vulnerabilities by using threat intelligence, organizational asset context, and risk modeling approaches such as attack path analysis. This is also an area in which advanced analytics methods are also being used, such as ML [machine learning]. This permits more granular and intelligent remediation strategies than the more simplistic severity approaches, especially at scale and when remediating with constrained resources.

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