AI-based Cyber Event OSINT via Twitter Data

Dale, Dakota; McClanahan, Kylie; Li, Qinghua

doi:10.1109/icnc57223.2023.10074187

Cited by 7 publications

(1 citation statement)

References 16 publications

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“…(Al-Dmour et al, 2023;Stone et al, 2023) demonstrate the application of AI in specific contexts: radiological event detection in war conditions and automated OSINT collection and management, respectively. Both studies highlight AI's potential in processing and analyzing large-scale, diverse data sets, a theme also explored by (Dale et al, 2023) in the context of aggregating Twitter (X) data for cybersecurity intelligence. The potential of AI in OSINT extends beyond cybersecurity, as shown by (Arroyo et al, 2023), who develop AI-supported tools for debunking scientific misinformation.…”

Section: Jan čErnýmentioning

confidence: 98%

Implications of Large Language Models for OSINT: Assessing the Impact on Information Acquisition and Analyst Expertise in Prompt Engineering

Černý

2024

eccws

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This paper explores the potential use of large language models (LLMs) in Open Source Intelligence (OSINT), with a focus on integrating information acquisition and the increasing importance of prompt engineering for analysts. The research includes a comprehensive literature review, which highlights the widespread use of AI in OSINT and the related challenges, such as data validity and ethical concerns. The study emphasizes the significance of prompt engineering as a crucial skill that demands a profound comprehension of LLMs to generate validated intelligence. A model of the OSINT lifecycle that incorporates LLMs is proposed. The paper further discusses updated training in critical thinking, search techniques, and prompt engineering for intelligence professionals. The findings indicate a noteworthy shift in OSINT procedures, highlighting the importance of continuous research and education to fully utilize AI in intelligence gathering.

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