AI-native IT architecture is fundamentally reshaping the modern enterprise and accelerating the shift toward decentralized autonomous networks. While this transition unlocks unprecedented capabilities, it also introduces a wave of complex security vulnerabilities. Today, enterprises face a tripartite challenge which are securing hyper-connected cloud environments, mitigating sophisticated cyber threats and ensuring algorithmic accountability. To address these interconnected issues, this study synthesizes recent literature (2022–2024) from Scopus and Web of Science bridging three disciplines typically studied in isolation of cloud governance, cybersecurity resilience and digital trust engineering. Technological adoption alone is insufficient for sustainable digital transformation. Organizations must pivot from reactive, post-deployment compliance to embedding proactive governance directly into their system architecture from inception. By examining dual-loop governance models and policy-as-code frameworks, this paper translates high-level theories into actionable deployment pathways and measurable performance indicators. Furthermore, as stringent regulations like the EU AI Act take effect, cyber defenses must evolve in tandem. Enterprises urgently require predictive resilience frameworks powered by Explainable AI (XAI) to pierce algorithmic opacity which is an effort that will rely heavily on quantitative trust assessments and decentralized identity infrastructures. Finally, to validate these concepts beyond theoretical constructs, we ground our proposed model in real-world application scenarios across the healthcare, finance and critical infrastructure sectors. Ultimately, for AI-native enterprises to achieve long-term viability, they must successfully balance rapid, autonomous innovation with adaptive security and verifiable stakeholder trust.
Uthayashankari A/P Shumugam, Nurazmiera Mhd Razali, Syed Mohsen Bin Syed Abu Bakar Alkaff et al.· International journal of res...· 0 citations
Cyber threats represent a critical risk to global organisations, with cybercrime damages projected to exceed USD 10.5 trillion annually by 2025. Consequently, enterprises are rapidly adopting Artificial Intelligence (AI) to augment their security architectures, as traditional, rule based Security Information Systems (SIS) struggle to mitigate sophisticated, multi stage attacks. However, the application of AI in security raises significant ethical questions around data privacy, algorithmic transparency, and the balance between automated surveillance and civil liberties. As a conceptual paper, this study investigates how businesses can govern AI-Driven security solutions by bridging the theoretical divide between technical efficacy and ethical responsibility. To build this theoretical foundation, a systematic review of 32 peer reviewed articles was conducted using the PRISMA paradigm, synthesizing existing evidence across four core governance dimensions: technical performance, stakeholder accountability, regulatory compliance, and organisational process. Our conceptual analysis reveals a persistent "principles to practices gap"; while AI based SIS significantly outperform traditional systems in anomaly detection and incident response, these technological advancements have outpaced the operationalization of ethical norms within organisations. To address this gap, the paper proposes a novel, unified governance framework centred on digital trust. This model distinctly integrates the AI Trust Framework and Maturity Model (AI TMM), the Tiered Ethical Cybersecurity Model (TECM), and privacy preserving technologies such as federated learning to operationalize ethics by design. The article concludes with actionable policy pathways for legislators, organisational leaders, and researchers to increase cyber resilience while strictly respecting individual privacy rights.
Muhammad Faris bin Nordin, Muhammad Din bin Khalid, Normal Mat Jusoh· International journal of res...· 0 citations
Global supply chains are passing through a period of unusual volatility, in which lean, efficiency-first models have repeatedly failed to absorb systemic shocks such as pandemics and geopolitical disruption. This paper reviews how artificial intelligence (AI) and three of its closest enabling technologies, namely the Internet of Things (IoT), digital twins, and predictive analytics, are being used to build supply chains that are both more resilient and more sustainable. Drawing on peer-reviewed journal research published within the last five years, the study brings together streams of work that are usually examined separately and asks how they fit together within the emerging vision of Industry 5.0 and increasingly autonomous supply chains. The synthesis indicates that AI shifts supply chain management from a reactive posture to an anticipatory one. IoT supplies the real-time visibility that supports proactive risk management, predictive and prescriptive analytics turn that data into early warning of demand shifts and disruption and into recommended action, digital twins allow managers to stress-test recovery options before acting on them, and blockchain adds a layer of trust to the data exchanged between partners. The review finds that the value of these technologies appears when they are integrated into a coherent, layered architecture rather than adopted in isolation, and that their contribution to resilience runs through underlying capabilities such as visibility, flexibility, and collaboration rather than through the technology alone. It also examines the obstacles that continue to slow adoption, including implementation cost, cybersecurity exposure, a persistent digital skills gap, interoperability with legacy systems, and the limited interpretability of complex models. The paper proposes a layered reference architecture for autonomous supply chains and sets out recommendations for practitioners and policymakers seeking a technology-driven, human-centric, and environmentally responsible supply network.
Koo Kee Wai, Lee Man Yi, Saw Tsu Koon et al.· International journal of res...· 0 citations
Although ERP has progressed from a closed, transactional records management platform to an open, intelligent system that learns, recommends and to an increasing degree acts, the conditions under which embedded AI capabilities translate into organizational agility remain insufficiently understood. The paper presents a theoretical synthesis on how changes in the financial responsiveness, the operational adaptiveness, and the governance posture of digital enterprises are transformed by AI-enabled cloud ERP, by drawing from a systematic analysis of a curated Scopus-indexed corpus. The review is organized around four established lenses, the Resource-Based View, Dynamic Capabilities Theory, Socio-Technical Systems Theory and the Technology Organization Environment framework. Merging these perspectives, the study provides a common conceptualization of a closed-loop and five stage decision pipeline with a five level ERP-AI maturity model. Three recurring themes can be seen in the literature. Embedded learning is linked to faster cycles towards financial close, improved responsive demand and risk planning, and a decreasing number of manual activities, but the degree of improvement is dependent on data quality, modular architecture, and governance maturity. Second, Enterprise platforms are among the strongest when they reflect different philosophies of architecture and not "veto points" for anything. Third, agility value is concentrated in companies that already have digital capabilities that they can leverage through their customer, cloud and commercially-available digital platforms, indicating that AI ERP is an add-on to a digital architecture, not a replacement. It provides theoretical research contributions within the domain of dynamic capability in the digital context, as well as a managerial diagnostic based on clean-core design, layered governance and maturity-aligned sequencing, and ends with 7 concrete directions for further research.
Lohgaindran Jeyeselan, Nurul Adha A Rihim, Zakiyah Awang et al.· International journal of res...· 0 citations
As industries move towards a circular economy, traditional Enterprise Resource Planning (ERP) systems struggle with a significant information gap during the product usage and end of life stages. To address this architectural limitation, this study highlights the essential system requirements for a Circular ERP (C-ERP) platform tailored for closed-loop resource management. Through a structured conceptual review of 30 high-impact studies retrieved from the Scopus database and a subsequent integrative thematic analysis, the research identifies three core capabilities for Circular ERP which are technological tracking, data interoperability, and organizational readiness. Guided by Dynamic Capabilities Theory, this study proposes an Integrated Circular ERP Architecture driven by closed loop feedback mechanism, Digital Product Passports (DPPs), AI-powered decision intelligence and Data Lakehouse infrastructures where ELT pipelines flow exclusively into the centralized platform. Ultimately, this architecture offers a strategic roadmap for shifting legacy transactional systems into unified coordination platforms across decentralized circular ecosystems while supporting the United Nations Sustainable Development Goals. The main contribution of this study is a theoretically grounded conceptual Circular ERP architecture that integrates fragmented lifecycle data into a unified Lakehouse platform that eliminates data fragmentation across the Beginning-of-Life (BOL), Middle-of-Life (MOL), and End-of-Life (EOL) phases. This framework practically supports manufacturers and policymakers by providing a structured architecture to guide future empirical validation through industrial case studies.
Nur Syakeerah Binti Ahmad Rezahan, Aln Elen Selvam A/L Superamaniam, A. Ramly et al.· International journal of res...· 0 citations
An exploratory literature review of the socio-technical issues involved in the adoption of GenAI tools in project documentation and governance and suggests a socio-technical conceptual framework that integrates these aspects into a unified view to inform people's understanding of responsible Human-AI collaboration in project settings.
Bela Lestari Dwireja, F. Abdalla, Yuhang Liu et al.· International journal of res...· 0 citations