Algorithmic Intelligence in Cyber-Resilient Solar and DevOps-Integrated Software Infrastructures: A Cross-Domain Framework for Sustainable Predictive Maintenance and Secure Automation

Abstract

The accelerating convergence of artificial intelligence, cloud-native software engineering, and cyber-physical energy infrastructures has generated a new epistemic and operational landscape in which algorithmic decision-making, predictive analytics, and automated orchestration increasingly determine the reliability, sustainability, and security of modern socio-technical systems. Within this evolving paradigm, AI-driven DevOps practices have emerged as a critical enabler of continuous deployment, real-time system observability, and adaptive maintenance, as extensively reviewed by Varanasi (2025), whose analysis demonstrates how machine learning-based intelligent automation has fundamentally reshaped software deployment and maintenance strategies. At the same time, the global expansion of solar photovoltaic systems and smart grids has produced unprecedented volumes of sensor data, exposing both immense opportunities for predictive maintenance and new vulnerabilities to cyber threats, as documented in diverse strands of the energy systems and cybersecurity literature (Engel and Engel, 2022; Rahman et al., 2018; Abdelkader et al., 2024).

Despite the apparent maturity of both AI-enabled DevOps and machine learning-driven photovoltaic maintenance, these two domains have largely evolved in parallel rather than in dialogue. Software engineering research has focused predominantly on optimizing deployment pipelines, reducing system downtime, and enhancing developer productivity through intelligent automation (Varanasi, 2025), while energy systems research has concentrated on fault detection, performance forecasting, and operational resilience of solar infrastructure (Ledmaoui et al., 2023; Nabti et al., 2022). Meanwhile, cybersecurity scholarship has emphasized the fragility of digitally networked power systems, highlighting how false data injection, IoT vulnerabilities, and adversarial attacks threaten the stability of smart grids (Unsal et al., 2021; Tufail et al., 2021). What remains insufficiently theorized is the systemic interaction between these three spheres: AI-driven DevOps, machine learning-based solar maintenance, and cybersecurity governance.

This article develops a comprehensive, theoretically grounded, and empirically informed framework that integrates these domains into a unified model of algorithmic infrastructure management. Drawing upon Varanasi’s (2025) account of intelligent DevOps pipelines, alongside contemporary studies on photovoltaic monitoring, predictive maintenance, and cyber risk modeling (Abdallah et al., 2023; Osmani et al., 2020; Rahim et al., 2023), the study conceptualizes modern digital-physical infrastructures as adaptive, learning-driven ecosystems rather than static technical artifacts. Through a qualitative meta-analytic methodology, the article synthesizes findings across software engineering, renewable energy systems, and cybersecurity, revealing how the epistemic logic of DevOps automation can be extended to solar microgrids and smart grid governance.