Artificial Intelligence in Hybrid Threat Operations: How AI Empowers the Existing Adversarial Playbook

The organising claim here — that AI structurally alters hybrid threats rather than merely adding to an existing toolkit — is the right level of analytical ambition, and the three-cluster framework (shaping societal perception, penetrating digital systems, directing effort) provides a cleaner taxonomy than most AI-and-security papers manage. By treating these as capability clusters rather than separate threat categories, Sigurðardóttir and Cvetko can capture the operational combinations that make AI-enabled hybrid operations qualitatively different from their pre-AI predecessors.

The regulatory gap observation is by now familiar but remains significant: AI capability development has continued to outpace both governance frameworks and analytical understanding of how adversaries are actually deploying these tools. For practitioners, the most useful section is the sensing-and-targeting cluster, which describes how AI enables adversaries to identify vulnerabilities, optimise targeting, and adapt in near-real-time — capabilities that compress the timelines available for detection and response in ways that existing counter-FIMI architectures were not designed to handle.