Energy infrastructure is becoming increasingly digital, connected, and AI-enabled. While AI can support predictive maintenance, optimization, and decision-making, it also introduces cybersecurity risks involving operational data, models, software dependencies, cloud services, and system integrations. This thesis examines these risks in the context of Renacore's intelligence platform for energy infrastructure.

The work combined academic and industry literature with analysis of Renacore’s platform and input from experts in energy, cybersecurity, and technology. The identified risks were then structured, assessed, and translated into a cybersecurity framework tailored to the requirements of AI systems in energy infrastructure.

The research identified key cybersecurity considerations that need to be addressed when AI-enabled systems are used in energy infrastructure. Based on these findings, a tailored cybersecurity framework was developed to support the structured identification, assessment, and mitigation of relevant risks.The framework brings together requirements from AI security, operational technology, cybersecurity governance, and the energy sector and translates them into a practical approach for secure system development and operation. A staged implementation roadmap complements the framework by outlining how appropriate cybersecurity measures can be introduced and strengthened over time.For Renacore, the results provide a practical basis for integrating cybersecurity into further platform development, supporting secure deployment in customer environments, and communicating a structured security approach to customers, partners, and other stakeholders.