Uchenna Daniel Ani

Generative Artificial Intelligence marks a critical inflection point in the evolution of machine learning systems, enabling the autonomous synthesis of content across text, image, audio, and biomedical domains. While these capabilities are advancing at pace, their deployment raises profound ethical, security, and privacy concerns that remain inadequately addressed by existing governance mechanisms. This study undertakes a systematic inquiry into these challenges, combining a PRISMA-guided literature review with thematic and quantitative analyses to interrogate the socio-technical implications of generative Artificial Intelligence. The article develops an integrated theoretical framework, grounded in established models of technology adoption, cybersecurity resilience, and normative governance. Structured across five lifecycle stages (design, implementation, monitoring, compliance, and feedback) the framework offers a practical schema for evaluating and guiding responsible AI deployment. The analysis reveals a disconnection between the fast adoption of generative systems and the maturity of institutional safeguards, resulting with new risks from the shadow Artificial Intelligence, and underscoring the need for adaptive, sector-specific governance. This study offers a coherent pathway towards ethically aligned and secure application of Artificial Intelligence in national critical infrastructure.

Socio‐Technical Security Modelling and Simulation (STSec‐M&S) is a technique used for reasoning and representing security viewpoints that include both the social and technical aspects of a system. It has shown great potential for improving the cybersecurity and resilience of Critical Infrastructure (CI). This study involved a multi‐methods approach, consisting of a scoping literature review and a focus group workshop, conducted with stakeholder engagement from critical infrastructure stakeholders to explore their perceptions and practices regarding the use of socio‐technical security modelling and simulation. The findings suggest that the current state of knowledge regarding the use and effectiveness of STSec‐M&Ss approaches is limited in CI domains. Consequently, there is little application of it in existing CI systems, regardless of its recognised benefits of enabling a better understanding of CI functionalities, security goals, early and more holistic risk identifications and selection of appropriate countermeasures. The benefits of the STSec‐M&S approach can be better realised by effective cross‐sector communications and collaborations, team partnerships, system and approach sophistication, and better security awareness amongst others. The potential barriers that can impede such benefits include high expense for implementing the technique, low data availability and quality, regulatory compliance, and competency gaps etc. Helpful recommendations include exploring and using realistic data, validating system security models, and exploring new ways of reskilling and upskilling CI stakeholders in socio‐technical security‐thinking and M&S approaches to enhance cybersecurity and resilience of CIs.

The Industrial Internet of Things (IIoT) trend presents many significant benefits for improving industrial operations. However, its emergence from the convergence of legacy Industrial Control Systems (ICS) and Information and Communication Technologies (ICT) has introduced newer security issues such as weak or lack of end-to-end security. These challenges have weakened the interest of many critical infrastructure industries in adopting IIoT-enabled systems. Implementing security in IIoT is challenging because this involves many heterogeneous Information Technology (IT) and Operational Technology (OT) devices and complex interactions with humans, and the environments in which these are operated and monitored. This article presents the initial results of the PETRAS Secure Ontologies for Internet of Things Systems (SOfIoTS) project, which consists of key security concepts and a modular design of a base security ontology, which supports security knowledge representation and analysis of IIoT security.