Time Table

With the escalating complexity of digital infrastructures, Attack Surface Management (ASM) has become an exceedingly crucial component for organizations striving to secure their assets. However, traditional ASM methodologies—including manual asset discovery, classification, and vulnerability management—are resource-intensive, prone to human error, and present significant challenges to effective ASM operations. In response, this presentation introduces an innovative approach that automates ASM processes using Large-scale Language Models (LLMs). By integrating LLMs into existing ASM workflows, our method seamlessly automates the identification and classification of internet-exposed assets, prioritization of detected vulnerabilities, and the proposal of strategies for vulnerability remediation. We will demonstrate the effectiveness of our proposed approach and emphasize how LLMs substantially contribute to the enhancement of ASM operations.