An increasingly aging population, widespread internet connectivity, and an increasing number of cybersecurity incidents bring many challenges for the future of information security. Older adults, in particular, are a highly vulnerable demographic, often targeted by cyber attackers. This paper aims to develop an approach that enables the identification of different groups of users of information systems according to their requirements for information and cyber security training (ICST). To achieve this, we surveyed 784 older adults residing in the UK recruited through the Prolific platform. First, S-HAIS-Q (Shortened Human Aspects of Information Security Questionnaire) was used to assess their information security performance. Through hierarchical clustering and k-means clustering, we identified five distinct groups. Additionally, predictive clustering trees (PCTs) were employed to simultaneously perform clustering and classification, providing deeper insights into the characteristics of these groups. This approach provides a segmentation framework highlighting the potential for customizing ICST to address the diverse needs of different groups of older adults. The results of our study indicate that it is possible to tailor and optimize ICST even among older adults who are more proficient in computer use than the general older adult population.