Escalation-Aware Governance Framework for Autonomous Warfare Decision Support Systems - Ni Luh Meliana Liberty; H.A.Danang Rimbawa; Bambang Suhardjo (Institute of Computer Science (IOCS), 2026)
Artificial intelligence increasingly transforms military decision-making through autonomous targeting, predictive battlefield analytics, cyber operations, and algorithm-assisted command systems. Although these technologies improve operational speed, they create governance risks when machine-speed decisions exceed human oversight, legal accountability, and escalation control. This study analyzes governance asymmetry in AI-driven autonomous warfare decision support systems and develops an escalation-aware governance framework. A structured qualitative review using transparent search procedures and thematic synthesis was conducted across 33 peer-reviewed and institutional sources on military AI, autonomous weapons, cybersecurity, and AI governance. The synthesis reveals three interconnected governance failures governance asymmetry, symbolic human control, and escalation compression that reinforce one another by weakening accountability, reducing substantive human judgment, and increasing strategic instability. The study proposes a human-centered escalation-aware governance framework integrating eight operational control layers: human authorization checkpoints, explainability, auditability, cybersecurity resilience, cognitive security, escalation-control mechanisms, emergency override, and post-action review. The framework contributes to intelligent decision support literature by translating autonomous warfare risks into embedded governance layers for preserving accountability and strategic stability