Augmented Data Management for Cache Performance, Cybersecurity, and Mobile Integration

Background: Database management systems now operate under workloads that legacy single-engine architectures were not designed to absorb. The combination of large-scale read-heavy applications, mobile and edge access patterns, and an expanding cybersecurity threat surface has pushed the field toward what vendors and researchers describe as augmented data management, in which artificial intelligence assists or replaces tasks that database administrators previously handled by hand.Objective: This review synthesizes the open-access literature on four converging strands: augmented data management with artificial intelligence in database systems, cache-augmented database architectures and cache replacement, artificial intelligence and blockchain applications in cybersecurity, and mobile, edge, and context-aware integration including augmented reality in education.Methods: The review follows the PRISMA 2020 reporting guideline. Records were identified through Scopus, Web of Science, IEEE Xplore, ACM Digital Library, Google Scholar, PubMed, and the Directory of Open Access Journals, covering 2000 to 2025. Eligibility required indexing in a recognized scholarly database, open-access availability, and a substantive contribution to at least one of the four strands. From 2,266 records identified, 1,854 remained after deduplication; 49 studies met the final inclusion criteria.Findings: The included studies show that learned components inside the database engine, predicate-based and semantic caching, adaptive replacement policies such as ARC and LIRS, distributed key-value caches at industrial scale, and offload-aware edge architectures have moved from isolated proposals to operational systems. The cybersecurity strand has consolidated around supervised, deep, and federated learning, while augmented reality has found traction in nursing and health education.Conclusion: Augmented data management is best understood as a stack rather than a single technology, with caching as the layer that ties the database engine to mobile and edge clients and with artificial intelligence supporting both performance optimization and security analytics. Persistent gaps include consistency under cache invalidation, reproducible benchmarks for learned indexes, and operational evidence for federated security models.