Authors - Ayushi Raj, Malathy C Abstract - The rapid growth of sensitive data requires backup systems that are both storage-efficient and risk-aware. Traditional backup approaches rely on static policies that ignore temporal changes, data sensitivity, and redundancy, leading to inefficient storage use and higher risk exposure. This work proposes a risk-adaptive backup optimization framework integrating temporal modelling, sensitivity-aware deduplication, and online learning. The system reconstructs data evolution using intrinsic timestamps and quantifies data criticality through continuous sensitivity scoring. A unified risk model combines sensitivity, change intensity, and exposure over time to determine backup urgency. An online rein forcement learning agent dynamically optimizes backup decisions based on evolving data patterns. The framework applies secure, sensitivity-based dedupli cation to reduce redundancy while preserving privacy. Operating in a read-only, metadata-driven manner, it ensures compliance with strict data governance re quirements. By decoupling decision logic from storage, the system supports hy brid cloud environments. Experimental results show reduced storage costs and controlled risk, demonstrating its effectiveness for scalable, intelligent data pro tection.
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