The ever-growing complexity of public financial systems and constraints of the conventional systems of internal control have increased the necessity of more responsive and even smarter methods of oversight. The present study is a synthesis of multidisciplinary literature that would create a combined conceptual framework of how data-driven monitoring systems would improve the internal financial controls within US government agencies. This study adopts a structured narrative review approach, systematically identifying and synthesizing recent peer-reviewed literature (2020–2025) across accounting, information systems, and public administration. Based on the theory of internal control and innovations in the field of big data analytics, machine learning, and continuous audits, the review shows that the capabilities related to data enable changing the traditional control systems that are characterized by being immobile, reactive systems into dynamic and real-time governance systems that enhance the ability to detect risks, promote transparency, and hold accountable. The paper also defines the main institutional drivers, barriers to implementation and governance issues that influence adoption and outlines gaps in empirical verification and AI regulation that need more careful consideration. With this review bridging the accounting, information systems, and public administration perspectives, the review adds value to the theory by expanding the internal control to the area of digital governance and provides valuable contributions to the policymakers and practitioners. The results emphasize that the future of financial governance by the state relies upon the strategic incorporation of information-based surveillance along with strong institutional and ethical frameworks.
H. A. K. Dankwah, Mary Magdalene Linda Yeboah· Journal of Economic, Finance...· 0 citations
Cybersecurity and information technology control frameworks in the United States exhibit significant fragmentation arising from overlapping regulatory mandates and duplicated controls. Audits and compliance activities in financial institutions and critical infrastructure sectors identify vulnerabilities but reveal limitations when applied as static mechanisms rather than adaptive processes. Mapping exercises between National Institute of Standards and Technology Cybersecurity Framework, Control Objectives for Information and Related Technology, and International Organization for Standardization 27001 consistently document both shared requirements and gaps that widen with the introduction of artificial intelligence and machine learning. Federal harmonization efforts have produced initial coordination yet face persistent barriers from agency-specific mandates and scarce longitudinal outcome data. Governance practices navigate these tensions through sector-specific applications that balance operational demands against systemic interoperability needs. The empirical studies highlight the need for continued attention to framework alignment, regulatory coordination, and empirical validation if resilience is to match evolving threats. Effective integration of controls requires addressing both practical implementation challenges and broader policy structures that shape cybersecurity governance across regulated industries. These dynamics underscore the importance of reducing unnecessary duplication while preserving essential specialization to support more resilient national cybersecurity posture. Ultimately, achieving meaningful harmonization will depend on sustained policy coordination and the development of robust evidence on post-alignment outcomes. This review synthesizes cross-sector evidence to identify structural, operational, and technological barriers to harmonization, while proposing an integrative governance perspective grounded in recent empirical and policy literature.
William Asare Yirenkyi, G. Apaflo, Matilda Konotey et al.· Magna Scientia Advanced Rese...· 0 citations