Various standards (e.g., ISO 27000x, ISO 31000:2018) and methodologies (e.g., NIST SP 800-53, NIST SP 800-37, NIST SP 800-161, ETSI TS 102 165-1, NISTIR 8286) are available for risk assessment. However, these standards often overlook the human element. Studies have shown that adversary profiles (AP), which detail the maturity of attackers, significantly affect vulnerability assessments and risk calculations. Similarly, the maturity of the users interacting with the Information and Communication Technologies (ICT) system in adopting security practices impacts risk calculations. In this paper, we identify and estimate the maturity of user profiles (UP) and propose an enhanced risk assessment methodology, HRM (based on ISO 27001), that incorporates the human element into the risk evaluation. Social measures, such as awareness programs, training, and behavioral interventions, alongside technical controls, are included in the Human-Centric Risk Management (HRM) risk treatment phase. These measures enhance user security hygiene and resilience, reducing risks and ensuring comprehensive security strategies in SMEs.

This paper presents the AI Trustworthiness Assessment Framework (AI_TAF), a comprehensive methodology for evaluating and mitigating trustworthiness risks across all stages of an AI system’s lifecycle. The framework accounts for the criticality of the system based on its intended application, the maturity level of the AI teams responsible for ensuring trust, and the organisation’s risk tolerance regarding trustworthiness. By integrating both technical safeguards and sociopsychological considerations, AI_TAF adopts a human-centric approach to risk management, supporting the development of trustworthy AI systems across diverse organisational contexts and at varying levels of human–AI maturity. Crucially, the framework underscores that achieving trust in AI requires a rigorous assessment and advancement of the trustworthiness maturity of the human actors involved in the AI lifecycle. Only through this human-centric enhancement can AI teams be adequately prepared to provide effective oversight of AI systems.

Collaborative learning has become an increasingly popular method of engaging students in the learning process. The activities included in this type of learning focus on the interaction that occurs between users when they are working in groups and they often incorporate solutions to real life problems. Collaborative software development to support this process has grown in popularity over the last years. In this paper, we evaluate software tools that are used in Collaborative Learning and investigate how these software tools can affect the learning process and to what extent they have influenced the educational process. In this study we focus on contemporary collaborative trends, their unique characteristics, and their assimilation and implementation to education and we propose learning activities that exploit their advantages. The results, among others, depict the significant extent of new technological achievements' effect on the educational process.

NERO represents a sophisticated Cybersecurity Ecosystem comprising five interconnected frameworks designed to deliver a Cybersecurity Awareness initiative, as advocated by ENISA as the optimal method for cultivating a security-centric mindset among employees to mitigate the impact of cyber threats. It integrates activities, resources, and training to nurture a culture of cybersecurity. NERO primarily equips SMEs with a repository of Cyber Immunity Toolkits, a Cyber Resilience Program, and Gamified Cyber Awareness Training, all accessible through a user-friendly Marketplace. The efficacy and performance of this concept will be affirmed through three distinct use case demonstrations across various sectors: Improving Patient Data Security in Healthcare with Cybersecurity Tools, Enhancing Supply Chain Resilience in the Transportation and Logistics Industry through Cybersecurity Awareness, and Elevating Financial Security via Enhanced Cybersecurity Awareness and Tools.