Johnathan Wilkes, John Anny (Palo Alto Networks)

By embracing automation, organizations can transcend manual limitations to reduce mean time to response and address exposures consistently across their cybersecurity infrastructure. In the dynamic realm of cybersecurity, swiftly addressing externally discovered exposures is paramount, as each represents a ticking time bomb. A paradigm shift towards automation to enhance speed, efficiency, and uniformity in the remediation process is needed to answer the question, "You found the exposure, now what?". Traditional manual approaches are not only time-consuming but also prone to human error, underscoring the need for a comprehensive, automated solution. Acknowledging the diversity of exposures and the array of security tools, we will propose how to remediate common external exposures, such as open ports and dangling domains. The transformative nature of this shift is crucial, particularly in the context of multiple cloud platforms with distinct data enrichment and remediation capabilities.

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ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Linkang Du (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Mingyang Sun (Zhejiang University), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (CISPA Helmholtz Center for Information Security and Stanford University)

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Facilitating Threat Modeling by Leveraging Large Language Models

Isra Elsharef, Zhen Zeng (University of Wisconsin-Milwaukee), Zhongshu Gu (IBM Research)

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