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How are LLMs with Endpoint Data Boost Cybersecurity

The issue of capturing weak signals across endpoints and predicting possible patterns of intrusion attempts is ideally suited for Large Language Models (LLMs). The objective is to mine attack data in order to improve LLMs and models and discover new threat patterns and correlations.

Recently, some of the top endpoint detection and response (EDR) and extended detection and response (XDR) vendors were seen taking on the challenge. 

Palo Alto Network’s chairman and CEO Nikesh Arora says, “We collect the most amount of endpoint data in the industry from our XDR. We collect almost 200 megabytes per endpoint, which is, in many cases, 10 to 20 times more than most of the industry participants. Why do you do that? Because we take that raw data and cross-correlate or enhance most of our firewalls, we apply attack surface management with applied automation using XDR.” 

Co-founder and CEO of Crowdstrike, George Kurtz stated at the company’s annual Fal.Con event last year, “One of the areas that we’ve really pioneered is that we can take weak signals from across different endpoints. And we can link these together to find novel detections. We’re now extending that to our third-party partners so that we can look at other weak signals across not only endpoints but across dom

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