Rethinking Ransomware Protection Targets for AI Systems

Artificial intelligence (AI) systems have become operational infrastructure whose value is increasingly dominated by trained models, behavioral configurations, and decision-making logic rather than by software binaries alone. As a result, ransomware threats against AI systems cannot be adequately addressed by conventional recovery strategies that assume service availability can be restored through file and code recovery. In AI environments, assets such as model parameters, training data, inference pipelines, and safety policies constitute primary attack targets, and their compromise can invalidate system behavior even when files are successfully restored. This study re-examines ransomware threats against AI systems from an asset-based protection perspective and demonstrates why traditional recovery assumptions structurally fail in AI-centric environments. Based on this analysis, we show that protection mechanisms limited to file integrity are insufficient and must be extended to include behavioral consistency and decision-making reliability. To address this gap, we propose a behavior-aware ransomware protection methodology, implemented as the Behavior-Aware Integrity Protection System (BIPS). BIPS augments existing ransomware response processes by redefining protection targets, establishing behavioral baselines, verifying post-recovery behavioral integrity, and supporting risk-based operational decisions. This work contributes by reframing ransomware threats against AI systems as an issue rooted in protection scope and recovery assumptions rather than isolated attack techniques, thereby extending ransomware response for AI systems toward a reliability- and risk-oriented protection framework.