Cyber security has made an impact and has challenged Small and Medium Enterprises (SMEs) in their approaches towards how they protect and secure data. With an increase in more wired and wireless connections and devices on SME networks, unpredictable malicious activities and interruptions have risen. Finding the harmony between the advancement of technology and costs has always been a balancing act particularly in convincing the finance directors of these SMEs to invest in capital towards their IT infrastructure. This paper looks at various devices that currently are in the market to detect intrusions and look at how these devices handle prevention strategies for SMEs in their working environment both at home and in the office, in terms of their credibility in handling zero-day attacks against the costs of achieving so. The experiment was set up during the 2020 pandemic referred to as COVID-19 when the world experienced an unprecedented event of large scale. The operational working environment of SMEs reflected the context when the UK went into lockdown. Pre-pandemic would have seen this experiment take full control within an operational office environment; however, COVID-19 times has pushed us into a corner to evaluate every aspect of cybersecurity from the office and keeping the data safe within the home environment. The devices chosen for this experiment were OpenSource such as SNORT and pfSense to detect activities within the home environment, and Cisco, a commercial device, set up within an SME network. All three devices operated in a live environment within the SME network structure with employees being both at home and in the office. All three devices were observed from the rules they displayed, their costs and machine learning techniques integrated within them. The results revealed these aspects to be important in how they identified zero-day attacks. The findings showed that OpenSource devices whilst free to download, required a high level of expertise in personnel to implement and embed machine learning rules into the business solution even for staff working from home. However, when using Cisco, the price reflected the buy-in into this expertise and Cisco’s mainframe network, to give up-to-date information on cyber-attacks. The requirements of the UK General Data Protection Regulations Act (GDPR) were also acknowledged as part of the broader framework of the study. Machine learning techniques such as anomaly-based intrusions did show better detection through a commercially subscription-based model for support from Cisco compared to that of the OpenSource model which required internal expertise in machine learning. A cost model was used to compare the outcome of SMEs’ decision making, in getting the right framework in place in securing their data. In conclusion, finding a balance between IT expertise and costs of products that are able to help SMEs protect and secure their data will benefit the SMEs from using a more intelligent controlled environment with applied machine learning techniques, and not compromising on costs.

Small and medium-sized enterprises (SMEs) continue to face significant cybersecurity challenges due to limited financial resources, technical capacity, and awareness. This study addresses these issues by pursuing four key objectives: (1) conducting a comprehensive assessment of cybersecurity knowledge and awareness within the SME sector through a systematic literature review, (2) evaluating the impact and effectiveness of cybersecurity awareness programs on SME behaviors and risk mitigation, (3) identifying core barriers—financial, technical, and organizational—that hinder effective cybersecurity adoption, and (4) introducing and validating the enhanced ROHAN model in conjunction with the Cyber Guardian Framework (CGF) to offer a scalable roadmap for cybersecurity resilience. Drawing on secondary data from Rawindaran (2023), the research highlights critical deficiencies in SME cybersecurity practices and emphasizes the need for tailored role-specific awareness initiatives. The enhanced ROHAN model addresses this need by delivering customized cybersecurity education based on industry sector, professional role, and educational background. Integrated with the CGF, the framework promotes structured, ongoing improvements across organizational, technological, and human domains. A mixed-methods approach was used, combining quantitative survey data from Welsh SMEs with qualitative interviews involving SME stakeholders. Advanced analytical techniques, including regression testing, Principal Component Analysis (PCA), and data visualization, were employed to uncover key insights and patterns. A distinctive feature of the ROHAN model is its integration of AI-powered tools for real-time risk assessment and decision-making, reflecting the principles of Industry 5.0. By aligning technological innovation with targeted education, this study presents a practical and adaptable cybersecurity framework for SMEs. The findings aim to bridge critical knowledge gaps and provide a foundation for a more resilient, cyber-aware SME sector in Wales and comparable regions.

As society advances toward Society 5.0, ubiquitous computing, IoT, and cloud-based analytics amplify the need for transparent, privacy-preserving cybersecurity governance particularly for small and medium enterprises (SMEs) that lack the resources and maturity of larger organisations. This study proposes a federated governance architecture that enables secure cross-organisational AI collaboration without centralising sensitive data. The model combines federated learning with blockchain-backed auditability to support data sovereignty, consent-aware participation, and tamper-evident accountability. A feedback-driven governance loop provides continuous monitoring and policy refinement to mitigate governance drift, while secure aggregation and proxy mediation reduce attack surfaces and regulatory exposure for SMEs. The proposed approach operationalises “compliance by design” through policy-driven enforcement, enabling SMEs to contribute to shared intelligence while retaining local control of high-risk datasets. The work outlines a viable pathway to a more secure, transparent, and socially equitable digital ecosystem aligned with Society 5.0 and emerging regulatory expectations.