Cybersecurity has moved from a peripheral technical function to a core pillar of organizational governance, driven by the escalating frequency and cost of digital intrusions, tightening disclosure regulation, and growing recognition that technical controls alone cannot guarantee continuity of operations. This narrative integrative review synthesises contemporary academic literature on cybersecurity governance, tracing its evolution from a compliance-oriented, risk-reporting paradigm toward an integrated model of organizational cyber resilience. The review examines governance structures and board oversight arrangements, the integration of cybersecurity into enterprise risk management, the conceptual architecture of organizational cyber resilience, the human and cultural determinants of governance effectiveness, sector-specific and supply-chain vulnerabilities, financial and insurance mechanisms for risk transfer, the regulatory and standards landscape, and approaches to measuring governance maturity. Findings indicate that although disclosure obligations and formal oversight structures have proliferated, substantive board-level expertise remains scarce, enterprise risk management integration is uneven, and resilience-building efforts are frequently undermined by fragmented accountability and inconsistent measurement practices. The review argues that a durable shift from reactive risk reporting to genuine organizational resilience requires coherent alignment across governance structures, cultural investment, supply-chain oversight and outcome-based metrics. Directions for future research and the practical implications of these findings for boards, risk officers and regulators are discussed.
William Asare Yirenkyi, Apaflo Godson Teye, Matilda Konotey et al.· Asian journal of current res...· 0 citations
The increasing sophistication of financial and cyber fraud has led national governments, banks and regulators globally to incorporate artificial intelligence (AI) into anti-fraud measures. This paper compares the reactive, decentralized approach adopted by the United States and more prescriptive, risk-based regulatory models embraced by global peers such as the European Union and the United Kingdom. By referencing academic studies, regulatory documents, and case studies of institutions, the paper covers how these tools (like machine learning (ML) models, biometric authentication, and real-time transaction monitoring) are used to detect and prevent fraudulent behavior, including identity theft and new generative-AI-driven scams. It also examines how different regulatory environments influence the adoption of AI and technology, with a focus on the intersection among innovation, compliance, privacy, and ethical governance.
The results indicate that the U.S. model, with its flexibility and quick adaptability by sectors, can result in fractured oversight structures and breaks in compliance and accountability. In contrast, international approaches, including the EU’s AI Act and UK proposals, emphasize transparency, standardization, and risk reduction, but may restrict innovation through stringent regulatory demands. Effective AI-enabled fraud prevention demands common international standards, ethical AI governance, and enhanced cross-border data sharing mechanisms. It serves as a hero to transform global financial security and regulatory collaboration in the age of intelligent fraud detection.
Afari Ntiakoh, Christian Amoakoh, Deborah Akuele Apaflo et al.· Journal of Economic, Finance...· 0 citations
Risk-based IT auditing and cybersecurity assurance have become central mechanisms for protecting regulated organizations amid evolving digital threats. This review synthesizes peer-reviewed literature on governance structures, auditing methods, and resulting outcomes across key sectors including financial services, capital markets, healthcare, and critical infrastructure. Drawing from a broad body of literature, it examines how regulatory frameworks shape risk identification and control deployment while highlighting assurance practices that contribute to measurable improvements in threat mitigation and compliance. The analysis reveals consistent emphasis on integrated governance approaches alongside persistent implementation tensions, such as mismatches between risk-based ideals and practical application. Key insights underscore the role of adaptive controls, outcome-focused assurance, and sector-specific adaptations in enhancing overall cybersecurity posture. The review also identifies areas where current practices fall short, offering grounded directions for advancing both theory and practice in regulated environments.
William Asare Yirenkyi, Apaflo Godson Teye, Matilda Konotey et al.· Magna Scientia Advanced Rese...· 0 citations
Emissions monitoring in the United States is being reconstructed around dense, heterogeneous sensing and algorithmic inference. Low-cost electrochemical and optical sensors, mobile platforms, aircraft-mounted imaging spectrometers, point-in-space continuous monitors, and polar-orbiting and geostationary satellites now generate observations at scales and frequencies that regulatory reference networks were never designed to provide, and machine learning increasingly mediates the step from raw signal to reported emission or exposure. This review critically appraises what that reconstruction has demonstrated, what remains uncertain, and where confidence is currently misplaced. Evidence was assembled through registry-based bibliographic searching and verified source-by-source, with emphasis on studies conducted in United States settings and published from 1995 onwards. Four findings recur across otherwise separate literatures. First, reported model performance is dominated by co-location conditions rather than deployment conditions, and cross-site, cross-season and cross-instrument transfer degrades in ways that headline coefficients of determination conceal. Second, blind controlled-release testing has become the strongest evidentiary design available for methane sensing and has no established equivalent in ambient sensor calibration, producing a marked asymmetry in the quality of validation evidence between adjacent fields. Third, heavy-tailed and intermittent emission distributions break the sampling assumptions embedded in both survey design and supervised learning, so that platform-specific estimates diverge for structural rather than analytical reasons. Fourth, fusion across platforms with different detection thresholds is frequently treated as additive when it is in fact a partial-detection problem requiring explicit statistical treatment. Errors in fused products are spatially structured and correlate with monitor density, so that inferential accuracy is lowest where policy attention is greatest. Priorities identified include standardised blind evaluation for ambient sensing, detection-probability-aware fusion, distribution-shift-resilient calibration, and reporting practices that expose spatial error structure rather than aggregate it away.
Emmanuel Kiplagat Teigong, Y. Magdalene· Asian journal of current res...· 0 citations