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Background
New solutions, old problems. Artificial intelligence (AI) and large language models (LLMs) are here to signal a new day in the cybersecurity world, but what does that mean for us—the attackers and defenders—and our battle to improve security through all the noise?
Data is everywhere. For most organizations, the access to security data is no longer the primary issue. Rather, it is the vast quantities of it, the noise in it, and the disjointed and spread-out nature of it. Understanding and making sense of it—THAT is the real challenge.
When we conduct adversarial emulation (red team) engagements, making sense of all the network, user, and domain data available to us is how we find the path forward. From a defensive perspective, efficiently finding the sharpest and most dangerous needles in the haystack—for example, easily accessible credentials on fileshares—is how we prioritize, improve, and defend.
How do you make sense of this vast amount of structured and unstructured data, and give yourself the advantage?
Data permeates the modern organization. This data can be challenging to parse, process, and understand from a security implication perspective, but AI might just change all that.
This blog post will focus on a number of case studies where we obtained data during our complex adversarial emulation engagements with our global clients, and how we innovated using AI and LLM systems to process this into structured data that could be used to better defend organizations. We will showcase the lessons learned and key takeaways for all organizations and highlight other problems that can be solved with this approach for both red and blue teams.
Approach
Data parsing and understanding is one of the biggest early benefits of AI. We h
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