Bridging the Gap: A Systematic Review of Cyber Conflict Forecasting Models and the Case for AI-Driven Dynamic Frameworks

Cyber conflict forecasting remains constrained by static models that overlook the integration of geopolitical context with technical indicators. This systematic literature review examines 58 studies (2010–2025) using PRISMA guidelines and an Input-Process-Output framework to classify approaches and identify key gaps. Quantitative methods dominate (67%), yet only 14% incorporate geopolitical variables, despite the political nature of cyber conflict. Major limitations include adversarial adaptation blindness (85% assume static behavior), coarse temporal granularity (72% use daily+ intervals), lack of uncertainty quantification (75%), and minimal modeling of cross-domain escalation (92% cyber-only focus). Strategic forecasting is rare, with just 14% providing long-term insights and 16% offering decision support. In response, we propose eight design principles for AI-driven frameworks, emphasizing multimodal integration, adaptive threat modeling, fine-grained temporal analysis, and human-AI collaboration. This work lays the groundwork for dynamic forecasting systems that better support proactive cyber defense strategy and national security planning.