Francis Hahn (University of South Florida), Mohd Mamoon (University of Kansas), Alexandru G. Bardas (University of Kansas), Michael Collins (University of Southern California – ISI), Jaclyn Lauren Dudek (University of Kansas), Daniel Lende (University of South Florida), Xinming Ou (University of South Florida), S. Raj Rajagopalan (Resideo Technologies)

Security Operations Centers (SOCs) are high-stress, time-critical environments in which analysts manage multiple concurrent tasks and depend heavily on both technical expertise and effective communication. This paper examines the integration of Large Language Model (LLM) technologies into an operational SOC using an anthropological, fieldwork-based approach. Over a six-month period, two computer science graduate researchers were embedded within a corporate SOC, guided by an internal advocate, to observe workflows and assess organizational responses to emerging technologies. We began with an initial demonstration of an LLM-based incident response tool, followed by sustained participant observation and fieldwork within the incident response and vulnerability management teams. Drawing on these insights, we co-developed and deployed an LLM-based SOC companion platform supporting root cause analysis, query construction, and asset discovery. Continued in-situ observation was used to evaluate its impact on analyst practices. Our findings show that anthropological and sociotechnical approaches, coupled with practitioner co-creation, can enable the nondisruptive introduction of LLM companion tools by closely aligning development with existing SOC workflows.

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Feng Luo (The Hong Kong Polytechnic University), Zihao Li (The Hong Kong Polytechnic University), Wenxuan Luo (University of Electronic Science and Technology of China), Zheyuan He (University of Electronic Science and Technology of China), Xiapu Luo (The Hong Kong Polytechnic University), Zuchao Ma (The Hong Kong Polytechnic University), Shuwei Song (University of Electronic Science and…

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Pallas and Aegis: Rollback Resilience in TEE-Aided Blockchain Consensus

Jérémie Decouchant (Delft University of Technology), David Kozhaya (ABB Corporate Research), Vincent Rahli (University of Birmingham), Jiangshan Yu (The University of Sydney)

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