An Integrated Adaptive Security Framework for Data Security Posture Management with AI-Driven Orchestration and Quantum-Resistant Capabilities

Abstract

Although cloud storage offers the much-needed scalability, flexibility, and economic efficacy, it also presents significant security and privacy issues, which are mostly caused by the fact that, since data are encrypted, the traditional plaintext search approaches are compromised. To overcome this shortcoming, we introduce the Enhanced Aware Record Maintenance (EARM) architecture, a compound, privacy-aware architecture that supports ranked, multi-keyword encrypted data retrieval in the cloud. The fundamental retrieval process of EARM is novel, incorporating ontology-based keyword mapping, enhanced association rule mining, and inner-product similarities to produce highly accurate and relevant search results. The framework deploys a strong multi-layer defense model, which integrates quantum-resistant cryptography (Zero-Knowledge Proofs/Homomorphic Encryption), high-grade cipher suites (AES/RSA), and granular Attribute-Based Encryption (ABE) access controls (such as geofencing and decentralized identity management). Moreover, EARM also brings intelligent defensive layers of AI-based orchestration, which include ransomware, intrusion, and anomaly detection, behavioral biometrics, and a tamper-proof blockchain audit trail. Secure file versioning, rollback, and an encrypted trash bin are other ancillary features that improve the system's resilience. Empirical analyses performed on actual data sets indicate that EARM maintains high privacy and high retrieval rates and suffers only a few system overheads, hence making it an effective and highly secure encrypted cloud storage solution for modern challenges.