AI-Controlled Modular Decoy Generation for Reconstruction-Resistant Hybrid and Multi-Cloud Storage Systems

Although cloud storage is widely trusted by users and enterprises, externally stored encrypted and fragmented data remain vulnerable to reconstruction and inference attacks following partial exposure. Existing decoy-based defenses often rely on static configurations or randomly generated artifacts that can be filtered during adversarial analysis. This paper presents an Artificial Intelligence (AI)-controlled modular decoy generation method to enhance reconstruction resistance in distributed storage systems. The method operates as a system-agnostic post-fragmentation layer and does not require modification of encryption or storage architecture. Given encrypted fragments as input, decoys are generated using a supervised Extreme Gradient Boosting (XGBoost) regression model that adapts decoy quantity based on system telemetry and resource conditions. Decoys maintain statistical alignment with real encrypted fragments in size and Shannon entropy characteristics. To address scalability, the method is evaluated across small, medium, and large deployments comprising up to 413 externally exposed fragments and compared against fixed-ratio (10%, 20%) and randomized baselines. Experimental evaluation demonstrates increased adversarial uncertainty without altering legitimate reconstruction procedures or encryption mechanisms. Kolmogorov–Smirnov analysis indicates no statistically significant difference between AI-generated decoys and real fragments, whereas baseline decoys produce significant deviations in size and entropy distributions, supporting reconstruction resistance at scale in multi-cloud environments.