arXiv:2606.27106v1 Announce Type: cross Abstract: We present a novel approach for applying Large Language Models (LLMs) to threat assessment in the context of foreign peacekeeping missions. Building on the PINPOINT project and its use case, the EU Monitoring Mission in Georgia, we combine an interdisciplinary risk-model with OSINT-based media collection and LLM-supported threat extraction. The proposed workflow maps media contents to mission-relevant threats, extracts structured information and applies several additional LLM-based processing steps to improve relevance and grounding. An evaluat
The rapid advancement and increased accessibility of LLMs enable their application to complex analytical tasks like threat assessment, coinciding with ongoing geopolitical instabilities requiring enhanced monitoring capabilities.
This development showcases the practical operationalization of AI to improve intelligence gathering and risk mitigation for critical international missions, potentially shaping future defence and security strategies.
The ability to rapidly process and extract structured threat information from open-source intelligence using LLMs offers a significant upgrade to traditional manual or less sophisticated analytical methods.
- · Defence intelligence agencies
- · Peacekeeping organizations
- · AI/ML developers
- · OSINT platforms
- · Traditional intelligence analysis firms
- · Manual data processing roles
LLM-driven analysis enhances the speed and accuracy of threat assessment for foreign missions.
Improved threat intelligence could lead to more effective peacekeeping operations and better troop safety.
The successful application in Georgia could set a precedent for broader integration of AI into military and diplomatic intelligence frameworks globally, potentially altering the balance of informational advantage.
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Read at arXiv cs.AI