The rapid convergence of artificial intelligence, Internet of Things infrastructures, and healthcare digitalization has placed unprecedented demands on hardware systems that are not only computationally powerful but also resilient, energy efficient, and diagnostically autonomous. Contemporary healthcare automation relies increasingly on distributed edge devices, embedded inference accelerators, and smart sensor networks, yet these technological ecosystems operate within global supply chains that are fragmented, geopolitically unstable, and vulnerable to disruptions in semiconductor manufacturing, logistics, and component sourcing. Within this context, the sustainability and reliability of AI-enabled healthcare infrastructure can no longer be conceptualized purely as a software or algorithmic problem; it must be understood as a deeply hardware-bound challenge shaped by supply chain resilience, diagnostic automation, and architectural adaptability. Building on recent developments in resilient AI hardware diagnostics and supply chain-aware design frameworks, this article develops a comprehensive theoretical and methodological synthesis that integrates hardware acceleration, emerging memory technologies, energy-aware embedded systems, and healthcare automation requirements into a unified analytical model. Central to this framework is the notion that diagnostic automation embedded directly into AI hardware platforms enables self-monitoring, fault isolation, and adaptive reconfiguration, thereby mitigating the risks posed by component variability and supply chain instability, as demonstrated by recent work on resilient AI hardware in fragmented supply chains (Chandra et al., 2026). Through an extensive engagement with the literature on hardware acceleration for machine learning, edge AI, ultra-low power microcontrollers, and healthcare-oriented automation systems, this study articulates how hardware-level diagnostics and architectural redundancy can be aligned with the operational imperatives of smart healthcare environments. The methodological approach is qualitative and theoretical, grounded in systematic interpretation of cross-disciplinary sources, enabling the construction of a conceptual results model that maps relationships among hardware resilience, energy efficiency, diagnostic intelligence, and healthcare service continuity. The findings suggest that resilient diagnostic automation is not merely a technical add-on but a structural necessity for sustainable healthcare AI ecosystems, especially when deployed in globally distributed and resource-constrained contexts. By critically analyzing competing scholarly perspectives and identifying unresolved tensions between performance optimization and supply chain robustness, the article contributes a long-term research agenda for hardware-centric sustainability in digital healthcare.

The rapid acceleration of cloud-native software delivery, microservice-oriented architectures, and continuous integration and deployment pipelines has fundamentally altered the operational fabric of modern software engineering. Organizations today no longer struggle only with writing correct code but with governing, monitoring, and evolving large-scale, continuously changing software ecosystems. Within this environment, the convergence of Artificial Intelligence for IT Operations and AI-driven DevOps has emerged as one of the most consequential paradigms of contemporary digital infrastructure management. This article develops a comprehensive theoretical and empirical synthesis of how AIOps and AI-driven DevOps jointly reshape software deployment, reliability engineering, and operational decision-making. Building on machine learning-based automation for deployment and maintenance articulated in the recent synthesis of AI-driven DevOps (Varanasi, 2025), this study integrates broader AIOps research on anomaly detection, log analytics, tracing, governance, and predictive reliability engineering into a single unified analytical framework.

The article positions AIOps not as a standalone toolset but as a socio-technical intelligence layer embedded within DevOps pipelines. Drawing on the extensive literature on log-based anomaly detection, time-series modeling, distributed tracing, and failure prediction, it argues that AI-driven DevOps represents a shift from reactive, human-centered operations toward proactive, data-centric and semi-autonomous operational governance. This transformation is examined historically, theoretically, and methodologically. Historically, the work situates AIOps within the evolution from traditional system administration to automated operations and continuous delivery. Theoretically, it draws on systems theory, reliability engineering, and organizational learning to conceptualize how machine learning alters the epistemology of operational knowledge. Methodologically, it develops a structured qualitative-analytical synthesis of prior empirical studies, surveys, and industrial case analyses.

Ultimately, the article contributes a comprehensive, integrative theory of AI-driven DevOps as a foundational pillar of modern software engineering. It proposes that future cloud enterprises will increasingly rely on algorithmic operational intelligence not merely to keep systems running, but to actively shape how software is designed, deployed, and evolved over time, consistent with the emerging evidence from AIOps research and industrial practice (Dang et al., 2019; Gulenko et al., 2020; Varanasi, 2025).

The rapid digital transformation of retail enterprises, driven by cloud adoption and microservices architectures, has intensified the need for robust security frameworks that ensure compliance, operational resilience, and resistance to emerging threats. Traditional DevOps practices, while effective for accelerating delivery, often lack sufficient mechanisms for ensuring security throughout the software development lifecycle. In response, the DevSecOps paradigm integrates security practices directly into DevOps workflows, shifting security left and embedding continuous verification within continuous integration and continuous deployment (CI/CD) pipelines. This research article examines the theoretical foundations, empirical practices, and emerging innovations in DevSecOps, with an emphasis on the retail cloud domain where compliance and resilience are particularly critical due to sensitive customer data and regulatory constraints. Drawing on established literature and recent advances, including strategies for secure DevOps in retail cloud environments (Gangula, 2025), this work synthesizes a comprehensive understanding of how security tools, machine learning, and systemic organizational strategies coalesce to strengthen cloud security.

The study critically engages with themes such as automated security verification, machine learning integration, microservices intrusion detection, and secure development methodologies. By weaving insights from systematic reviews, architectural proposals, and methodology papers, the research illuminates the multifaceted challenges and practical solutions in contemporary DevSecOps adoption. Findings suggest that while automation and AI/ML tools provide substantial gains in threat detection and compliance monitoring, organizational culture, metrics frameworks, and model‑based security design play indispensable roles. Furthermore, limitations remain in adequately securing dynamic microservices environments and aligning DevSecOps practices with evolving regulatory frameworks. The article concludes by proposing future research directions focused on adaptive security frameworks, enhanced interpretability of AI models, and cross‑domain integrations capable of addressing emerging cyber threats in retail cloud infrastructures.