Why Training LLMs With Endpoint Data Will Strengthen Cybersecurity

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Capturing weak signals across endpoints and predicting potential intrusion attempt patterns is a perfect challenge for Large Language Models (LLMs) to take on. The goal is to mine attack data to find new threat patterns and correlations while fine-tuning LLMs and models. Leading endpoint detection and response (EDR) and extended detection and response (XDR) vendors are taking on the challenge.

Nikesh Arora, Palo Alto Networks chairman and CEO, said, “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.”

CrowdStrike co-founder and CEO George Kurtz told the keynote audience 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 domains and come up with a novel detection.”….Story continues..

Source: Why training LLMs with endpoint data will strengthen cybersecurity | VentureBeat