AI-Driven Just-In-Time Cryptographic Framework with Adaptive Protection of Data in Cloud and Edge Environments

Abstract

The rapid growth of cloud and edge computing has increased the challenge of protecting Personally Identifiable Information (PII) during runtime, where conventional encryption leaves data exposed while in use. Existing approaches rely on static encryption, fixed access policies, or resource-intensive security mechanisms, limiting adaptability to dynamic threats and compliance requirements. This paper proposes an AI-driven, context-aware Just-In-Time (JIT) cryptographic framework that combines machine learning-based risk assessment with adaptive encryption selection. Four classifiers (Random Forest, XGBoost, SVM, and Neural Network), trained on CICIDS2017, CICIoT2023, and UNSW-NB15 datasets, generate real-time risk scores based on user behavior, data sensitivity, and execution context. Based on risk levels, the framework dynamically applies QKD-enhanced AES-256-GCM, AES-256-GCM with RSA-4096, ChaCha20-Poly1305, or tokenization/masking, while zero-trust access control ensures secure data governance. Evaluation on 1,000 synthetic PII records across nine deployment environments achieved up to 98.74% classification accuracy, 11.3 ms average JIT encryption latency (6.5 ms at the edge), 92.5/100 security score, and 100% regulatory compliance, outperforming static encryption frameworks with 30% higher security and lower latency. The results demonstrate an efficient, scalable, and regulation-compliant approach for adaptive PII protection in modern cloud-edge environments.