This study examines cybersecurity and privacy challenges in Ecuador's Energy Information Systems (EIS), focusing on Bayesian models for risk assessment and mitigation. Ecuador's energy sector has faced increasing cyber threats, including Distributed Denial of Service (DDoS) attacks, ransomware, and phishing incidents, jeopardizing critical infrastructure stability. Utilizing monthly cyber threat data specific to Ecuador's energy sector, Bayesian networks were constructed to model the probability of various cyber incidents. This approach incorporates prior knowledge and updates probabilities as new data emerges, offering a dynamic risk assessment framework. The analysis identifies key vulnerabilities within Ecuador's EIS, such as the integration of distributed energy resources and the increasing digitalization of control systems, which have expanded the attack surface for potential cyber threats. By applying Bayesian analysis to the monthly cyber threat data, the study assesses the likelihood of different attack vectors and evaluates the effectiveness of various countermeasures. The findings provide insights into enhancing the resilience and security of Ecuador's energy infrastructure. By quantifying the probabilities of specific cyber threats and the potential impact of mitigation strategies, stakeholders can make informed decisions to strengthen cybersecurity measures. This study underscores the importance of adopting advanced analytical tools, such as Bayesian models, in developing proactive and adaptive cybersecurity strategies tailored to the evolving threat landscape in Ecuador's energy sector.