Interoperable data and intelligence flows among allied partners and operational end-users remain essential to NATO's collective defense across both conventional and hybrid threat environments. Foreign Information Manipulation and Interference (FIMI) increasingly spans multiple societal domains and information ecosystems, complicating threat characterization, persistent situational awareness, and coordinated response. Concurrent advances in AI have further lowered the barrier to conducting large-scale, AI-augmented FIMI activities -- including automated generation, personalization, and amplification of manipulative content. While frameworks such as DISARM offer a standardized analytical and metadata schema for characterizing FIMI incidents, their practical application for automating large-scale detection remains challenging. We present a framework-agnostic, agent-based operationalization of DISARM piloted to support FIMI investigation on social platforms. Our agent coordination pipeline integrates general agentic AI components that (1) identify candidate manipulative behaviors in social-media data and (2) map these behaviors to DISARM taxonomies through transparent, auditable reasoning steps. Evaluation on two practitioner-annotated, real-world datasets demonstrates that our approach can effectively scale analytic workflows that are currently manual, time-intensive, and interpretation-heavy. Notably, the experiment surfaced more than 30 previously undetected Russian bot accounts -- deployed for the 2025 election in Moldova -- during the prior non-agentic investigation. By enhancing analytic throughput, interoperability, and explainability, the proposed approach provides a direct contribution to defense policy and planning needs for improved situational awareness, cross-partner data integration, and rapid assessment of information-environment threats.

翻译：盟国伙伴及作战最终用户间的可互操作数据与情报流动，对于北约在常规与混合威胁环境下的集体防御仍至关重要。外国信息操纵与干扰（FIMI）日益跨越多个社会领域与信息生态系统，使威胁特征刻画、持续态势感知及协调响应变得复杂。人工智能的并行进展进一步降低了开展大规模AI增强型FIMI活动的门槛——包括操纵性内容的自动生成、个性化定制及传播放大。尽管诸如DISARM等框架为描述FIMI事件提供了标准化的分析与元数据模式，但其在自动化大规模检测中的实际应用仍具挑战性。我们提出一种框架无关的、基于智能体的DISARM操作方法，并在社交平台上试点支持FIMI调查。我们的智能体协调流水线集成了通用智能体AI组件，这些组件能够（1）识别社交媒体数据中的候选操纵行为，并通过透明、可追溯的推理步骤将这些行为映射至DISARM分类体系。对两个经从业者标注的真实世界数据集进行的评估表明，我们的方法可有效扩展当前依赖人工、耗时且高度依赖解读的分析工作流。值得注意的是，该实验在先前非智能体调查中额外发现了30多个此前未检测到的俄罗斯机器人账户——这些账户曾被用于摩尔多瓦2025年大选。通过提升分析吞吐量、互操作性与可解释性，所提方法直接满足了国防政策与规划需求，有助于改进态势感知、跨伙伴数据集成及信息环境威胁的快速评估。