Cross-Domain AI Architecture for Secure Automation and Predictive Maintenance in Solar Infrastructure and DevOps-Enabled Cyber-Resilient Systems
Keywords:
Cognitive Automation, DevOps Optimization, Machine Learning, Enterprise Software EngineeringAbstract
The convergence of artificial intelligence, renewable energy systems, and DevOps-driven software engineering has created a new class of cross-domain infrastructures requiring unified intelligence, security, and predictive operational capabilities. This research proposes a cross-domain AI architecture that integrates predictive maintenance for solar photovoltaic (PV) systems with DevOps-enabled cyber-resilient automation frameworks. The study synthesizes advancements in machine learning-based solar monitoring (Engel & Engel, 2022), cybersecurity threat modeling in smart grids (Rahim et al., 2023), and intelligent automation in software deployment pipelines (Varanasi, 2025). The proposed architecture introduces a layered intelligence model combining data ingestion, anomaly detection, predictive analytics, and autonomous remediation across both physical and digital infrastructure layers.
The framework addresses two core challenges: (1) inefficiencies in predictive maintenance of solar PV systems due to fragmented monitoring systems (Kalay et al., 2022), and (2) increasing cybersecurity vulnerabilities in interconnected smart grids and DevOps pipelines (Tufail et al., 2021). By integrating AI-driven monitoring systems (Abdallah et al., 2023) with adaptive cybersecurity mechanisms (Unsal et al., 2021), the architecture enables real-time decision-making, automated fault correction, and resilient system orchestration.
A key contribution of this work is the unification of operational intelligence across energy and software domains, allowing continuous feedback loops between physical infrastructure and digital control systems. The study
further incorporates inclusive AI interpretability considerations inspired by linguistic and identity-aware AI systems (Azizov, 2023), ensuring that AI-driven decision systems remain transparent and socially aligned.
The findings suggest that cross-domain AI architectures significantly enhance system resilience, reduce downtime, and improve predictive accuracy in both solar energy and DevOps environments. However, challenges remain in interoperability standardization, data heterogeneity, and adversarial resilience.
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