LogicHub Named a Demand-Side Innovator for AI in Attack Detection in 2021 Gartner® Report

LogicHub today announced it has been named a Demand-Side Innovator in the 2021 Gartner report, Emerging Technologies: Tech Innovators in AI in Attack Detection – Demand Side [1].

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“We take pride in pushing the envelope of what’s possible in applying AI to meet security challenges”

In the report, Gartner writes that “Timely detection of attacks remains a pertinent challenge in the cybersecurity industry today, despite organizations having a large amount of data generated by multiple solutions that typically work in isolation. The burden of manually correlating this data and analyzing it to look for attack signals falls on already stretched security analysts. AI- and machine learning (ML)-based solutions can rapidly sift through large amounts of data to look for weak signals that these overstretched analysts otherwise would have missed.”

“We take pride in pushing the envelope of what’s possible in applying AI to meet security challenges,” said Kumar Saurabh, CEO and co-founder of LogicHub. “We have based our platform on expertise automation and a blend of expert systems with deep neural net architecture, designed to work with a reduced amount of data. This fully customizable platform provides the backbone of our unique managed detection and response (MDR) services.”

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Saurabh added, “We believe that this third-party industry analysis provides a significant validation of our approach, as the security industry is long-overdue for real innovation and the practical application of emerging technologies around automation, machine learning, and artificial intelligence for attack detection.”

Gartner analysts specifically address this gap recommending that product leaders focused on emerging technologies’ and trends’ impact on products and services “should improve adoption of AI-enabled solutions by moving away from a ‘black-box’ approach toward explainable and customizable AI models that can be tuned based on analyst feedback.”

While not all security experts can build complex playbooks, experienced analysts can often recognize malicious activity when they see it. This point is valid, and well-designed AI systems should take advantage of human experience and constantly use human input to improve accuracy. Gartner calls this out as a key need in AI Attack Detection as it states, “The capturing of the skills, expertise and techniques of security analysts for use cases, such as data labeling, threat hunting, automated investigation, and response and remediation.”

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