Selecting appropriate information security systems is a complex task influenced by organizational risks, infrastructure characteristics, evolving cyber threats, and regulatory requirements. Traditional approaches based on manual analysis and expert judgment often lack adaptability, scalability, and consistency in dynamic cybersecurity environments. This study proposes a hybrid intelligent decision-support system that combines fuzzy logic, machine learning, and ISO-based cybersecurity standards for risk assessment and security action recommendation. The fuzzy inference module evaluates cybersecurity events under uncertainty using parameters such as attack frequency, vulnerability level, and defense efficiency. Based on the calculated risk level, the system generates standardized response actions aligned with international ISO/IEC standards. In addition, a Random Forest machine learning model is used to evaluate whether the selected actions are likely to mitigate the attack successfully. Experimental evaluation performed on a synthetic dataset demonstrated that the proposed hybrid architecture achieves high predictive performance and improves both interpretability and adaptability of cybersecurity decision support systems in complex operational environments.