AI-Driven Cloud-Native Microservices Framework for Secure Digital Communication, Software Reliability, and Enterprise Deployment Optimization
DOI:
https://doi.org/10.64137/31079911/IJMST-V2I1P106Keywords:
Artificial Intelligence, Cloud Native Systems, Microservices, Secure Digital Communication, Software Reliability, Deployment Optimization, Anomaly Detection, Zero Trust, Devsecops, Enterprise ArchitectureAbstract
Secure digital communication has become a foundational requirement for modern enterprises that operate across distributed cloud platforms, regulated data environments, and high-velocity software delivery pipelines. However, many organizations continue to rely on fragmented communication services, monolithic security controls, reactive monitoring, and manually governed deployment processes that are insufficient for dynamic microservices environments. The increasing adoption of cloud native architectures improves modularity and scalability, but it also expands the operational attack surface, increases service dependency complexity, and creates new reliability risks across application, infrastructure, and communication layers. This paper proposes an AI driven cloud native microservices framework for secure digital communication, software reliability, and enterprise deployment optimization. The framework integrates microservice decomposition, zero-trust communication controls, AI-assisted anomaly detection, service dependency intelligence, deployment telemetry, and reliability-aware governance into a vendor-neutral reference architecture. The major contribution of this paper is a structured architectural model that connects secure communication, predictive software quality, and continuous deployment optimization within a single enterprise-oriented framework. The paper also compares conventional microservice practices with AI augmented cloud native approaches and identifies practical gaps in observability, security automation, root cause analysis, and deployment governance. The analysis indicates that AI-driven microservices frameworks can support proactive reliability engineering, improved communication security, and more adaptive deployment decisions when implemented with appropriate governance, monitoring, validation, and human oversight. The paper is conceptual and architecture-based; therefore, it does not claim experimental proof. Instead, it offers a research-grounded model that can guide enterprise implementation, comparative evaluation, and future empirical validation.
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