Context-Aware Crypto-Orchestration in Cloud Environments: Scalable and Intelligent Protection

The explosive growth of cloud-hosted data has intensified concerns over confidentiality and integrity, while the increasing sophistication of cyberattacks exposes the fragility of algorithmic monocultures. Conventional symmetric ciphers such as AES deliver strong security, yet their performance and rigidity make them ill-suited for heterogeneous, multi-tenant environments. Recent hybrid and lightweight schemes improve efficiency but remain essentially static, lacking the ability to adapt cryptographic protection dynamically to workload context. This paper introduces a context-aware orchestration framework that integrates a formal risk--latency scoring model with ensemble machine learning for per-file cryptographic selection. The scoring function accounts for algorithmic strength, vulnerability history, data sensitivity, and latency tolerance, while the learning ensemble (CatBoost, Random Forest, Logistic Regression) reproduces expert decisions in real time. Experiments on 46,200 multimedia and textual files demonstrate 91.36% classification accuracy and macro-F1 of 0.85, with inference latency of only 0.1 ms and throughput of 102 MB/s. Compared with state-of-the-art baselines, our approach achieves higher accuracy and stability under noise and drift while reducing computational overhead. The framework is lightweight, scalable, and designed for seamless integration with emerging post-quantum standards, offering a practical path toward crypto-agile cloud infrastructures. By reframing encryption as a real-time decision process, it advances both the security and performance of distributed data services.