Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3975 2019 2019 Department of Physics, College of Science, University of Wasit, Wasit, Iraq Ahmed Ahmed Department of Electronics Techniques, Babylon Technical Institute, Al-Furat Al-Awsat Technical University, Babylon 51001, Iraq Ahmed M. Ali Ali This study examines the potential benefits of AI. It also addressees enhancing the performance of plants powered by solar and defending them against cyberattacks. Old controllers like PID and fuzzy logic work well in old places, and have no built in protection against cyber hackers that want to steal data, get into your control system, or obtain system access credentials. Artificial Neural Networks (ANN) and Reinforcement Learning (RL) are instances of AI-driven pattern stick to establishing fast adjustments on the fly, thus inducing non-normal behavior in controllers. This work uses AI to build models that predict solar flux on a surface and adjust input parameters in real time. In addition, it delivers security sensitive capabilities through pattern-driven analysis and alerting. MATLABSimulink simulations are used to demonstrate the efficacy of the approach, and it is compared with different methods in terms of power generation, time of response, power loss, stability, and quality of control. The ANN model made very good predictions, and the RL methods increased the flexibility and security of the system. According to the outcomes, the inclusion of AI into the system not only makes it more efficient in terms of producing energy but also renders it invulnerable to hackers or any other operational risks. This blog post discusses the need to secure AI-based energy systems with intelligent security. It also adds that future studies should explore the convergence of AI and cyber security in safeguarding critical infrastructure. 2026 2026 51 61 10.54216/JCIM.170105 https://www.americaspg.com/articleinfo/2/show/3975