The interoperability of data and intelligence across allied partners and their respective end-user groups is considered a foundational enabler of the collective defense capability -- both conventional and hybrid -- of NATO countries. Foreign Information Manipulation and Interference (FIMI) and related hybrid activities are conducted across various societal dimensions and infospheres, posing an ever greater challenge to threat characterization, sustained situational awareness, and response coordination. Recent advances in AI have further reduced the cost of AI-augmented trolling and interference activities, such as through the generation and amplification of manipulative content. Despite the introduction of the DISARM framework as a standardized metadata and analytical framework for FIMI, operationalizing it at the scale of social media remains a challenge. We propose a framework-agnostic, agent-based operationalization of DISARM to investigate FIMI on social media. We develop an agent coordination pipeline in which specialized agentic AI components collaboratively (1) detect candidate manipulative behaviors and (2) map these behaviors onto standard DISARM taxonomies in a transparent manner. We evaluate the approach on two real-world datasets annotated by domain practitioners. Our results show that the approach is effective in scaling the predominantly manual and heavily interpretive work of FIMI analysis -- including uncovering more than 30 previously undetected Russian bot accounts during manual analysis -- and provides a direct contribution to enhancing situational awareness and data interoperability in the context of operating in media- and information-rich settings.

翻译：数据与情报在盟国伙伴及其各自终端用户群体间的互操作性，被视为北约国家集体防御能力（包括常规与混合防御）的基础赋能要素。外国信息操纵与干预（FIMI）及相关混合活动在多个社会维度和信息空间展开，对威胁特征刻画、持续态势感知及响应协调构成日益严峻的挑战。人工智能的最新进展进一步降低了AI增强型网络煽动与干预活动的成本，例如通过生成和放大操纵性内容。尽管DISARM框架已作为FIMI标准化元数据与分析框架被引入，但在社交媒体规模上实现其操作化仍面临困难。我们提出一种与框架无关、基于智能体的DISARM操作化方法，用于调查社交媒体上的FIMI活动。我们开发了一套智能体协同流程，其中专业化的智能体AI组件通过协作：（1）检测潜在的操纵行为；（2）以透明方式将这些行为映射至标准DISARM分类体系。我们在两个由领域专家标注的真实数据集上对该方法进行评估。结果表明，该方法能有效扩展目前以人工为主且高度依赖解读的FIMI分析工作——包括在人工分析过程中发现30余个先前未检测到的俄罗斯机器人账号——并为提升媒体与信息密集环境下的态势感知能力和数据互操作性做出直接贡献。