QUANTUM-RESISTANT CRYPTOGRAPHIC PROTOCOLS INTEGRATED WITH AI FOR SECURING CLOUD AND IOT ENVIRONMENTS

Abstract

This quantitative study investigated the performance, efficiency, and security resilience of quantum-resistant cryptographic protocols integrated with artificial intelligence (AI) across cloud and Internet of Things (IoT) environments. The research aimed to empirically assess whether AI-enhanced cryptographic systems could outperform conventional post-quantum algorithms in encryption throughput, latency, resource optimization, and security robustness. A factorial experimental design was implemented, encompassing multiple algorithmic classes—lattice-based, hash-based, code-based, and multivariate polynomial systems—under both AI-integrated and non-AI configurations. The analysis incorporated 7,200 experimental runs executed under varying workloads, environments, and simulated attack conditions. Linear mixed-effects models, correlation analysis, and reliability testing were used to validate the statistical integrity of the results. The descriptive analysis indicated that AI-augmented frameworks achieved consistently higher encryption speeds, lower decryption latency, and superior throughput-adjusted security efficiency compared to traditional post-quantum systems. Correlation analysis revealed strong positive relationships between AI detection accuracy, encryption performance, and system stability, confirming that AI optimization significantly improved operational consistency. Reliability and validity tests showed high internal consistency, with Cronbach’s alpha coefficients exceeding 0.90, and factor analysis confirmed that performance indicators loaded strongly on the intended theoretical constructs of cryptographic performance and AI adaptability. Collinearity diagnostics verified the independence of predictors, with all variance inflation factors below 2.0. Regression analysis demonstrated that AI integration was a statistically significant predictor of improved cryptographic outcomes (p < 0.001), increasing throughput efficiency by over 14% on average while reducing latency and energy consumption. The findings confirmed the primary hypothesis that AI-driven cryptographic optimization enhances both computational efficiency and system resilience against classical and quantum attack simulations. Lattice-based and code-based cryptosystems showed the most substantial performance gains when combined with AI learning models. Overall, the results validated that intelligent, adaptive encryption frameworks achieve measurable, statistically significant advantages in performance, scalability, and security across both cloud and IoT domains. These findings provide empirical evidence supporting the integration of AI-based decision systems into post-quantum cryptography for secure and sustainable digital infrastructures.