AI-Driven Vulnerability Identification and Prioritization

Abstract

The time-boxed nature of traditional security engagements, such as vulnerability assessments and penetration tests, poses a challenge when facing a large attack surface. The necessary reliance on security assessment tools often leads to an overwhelming number of minor vulnerabilities and, most importantly, to the oversight of critical vulnerabilities.

To tackle this problem, this presentation introduces an innovative approach that leverages artificial intelligence, specifically large language models (LLMs), to enhance contextual analysis and risk prioritization of potential threats across large domain scopes.

The developed tool addresses the limitations of traditional security engagements by incorporating AI-driven contextual analysis to interpret web pages from a penetration tester's perspective and further analyze the results from a security analyst's viewpoint. These limitations include the challenge of eliminating false positives, avoiding the detection of misconfigurations that have no real impact, accurately analyzing the context for sensitive information, and detecting only impactful vulnerabilities. A key feature of the tool is its ability to rank domains based on their likelihood of exploitation, providing security analysts with actionable insights that go beyond traditional severity ratings, such as CVSS scores. These insights ensure effective time management, resource allocation, and budget savings, allowing the most critical threats to be addressed promptly.

The aim of the presentation is to demonstrate the transformative potential of AI in cybersecurity by addressing specific challenges encountered in daily operations. Our goal is to inspire cybersecurity professionals to effectively utilize AI, thereby enhancing productivity and strengthening the security of the digital landscape.