The fast digitalization of solar, especially enabled by smart grid technology, IoT devices and cloud-based monitoring platforms has drastically augmented the susceptibility of photovoltaic (PV) systems to cyber-attacks. Cybersecurity has become a strategic concern while solar energy is becoming part of national and critical infrastructure with paramount importance. In this paper, we investigate the contribution of AI to improve cybersecurity resilience in smart solar energy systems. It provides a holistic view of combining machine learning, deep learning and anomaly detection for the purpose of spotting, predicting, and mitigating cyberattacks in communication networks, SCADA systems and inverter controllers. An AI hybrid model integrating convolutional and recurrent neural networks (CNN–RNN) has been proposed to detect intrusions in real-time by the analysis of operational data flows patterns. Simulation experiments with benchmarking datasets indicated 97% [Formula: see text] detection accuracy according to corresponding metric, which largely reduced the false-positive rate in contrast to the traditional rule-based systems. In addition, the paper presents AI-based adaptive response mechanisms that facilitate autonomous containment of threats and self-healing of systems. Results imply that AI may greatly enhance the cybersecurity immunity of smart solar grids, through proactive threat intelligence, automatic incident response and resilient system recovery. We end the paper with suggestions for incorporating AI-based cybersecurity paradigms in national renewable energy policies, and the future of research on explainable-ethical AI for sustaining energy security.