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

Context-Aware Crypto-Orchestration in Cloud Environments: Scalable and Intelligent Protection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Context-Aware Crypto-Orchestration in Cloud Environments: Scalable and Intelligent Protection Akram LICHANI, Radouane NOUARA, Nabil BELALA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7510384/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Cloud Security Context-Aware Encryption Crypto-Agility DPost-Quantum Cryptography Adaptive Cryptographic Orchestration Distributed Data Protection Scalable Cloud Infrastructures Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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