Adopting Cloud Computing and Big Data Analytics to Enhance Public Sector Transparency and Accountability Through Artificial Intelligence
Abstract
The integration of cloud computing and big data analytics with artificial intelligence (AI) offers transformative opportunities for public sector organizations to enhance transparency and accountability. As governments strive to maintain citizen trust, improve service delivery, and ensure the ethical use of public resources, technological advancements are increasingly seen as pivotal tools. Cloud computing provides the scalability and accessibility needed to handle vast amounts of public data, while big data analytics processes this information to uncover patterns, detect fraud, and optimize decision-making. AI, when layered on these technologies, facilitates predictive analysis, real-time insights, and automated systems for monitoring governance practices. This paper explores the synergy of these technologies in reshaping public administration, focusing on their role in enhancing operational transparency, combating corruption, and empowering citizens. However, while these advancements hold great promise, challenges such as data security, privacy concerns, and the digital divide pose significant obstacles. The paper concludes by proposing actionable strategies to overcome these challenges and maximize the potential of cloud-based AI systems in ensuring public sector accountability.
References
[1] D. Samaan and A. Tursunbayeva, “Fluid workforce management in the health sector: navigating the changing face of workforces and their management,” Int. J. Publ. Sect. Manag., vol. 37, no. 4, pp. 593–609, Jun. 2024.
[2] S. V. Bhaskaran, “Enterprise Data Ecosystem Modernization and Governance for Strategic Decision-Making and Operational Efficiency,” Quarterly Journal of Emerging Technologies and Innovations, vol. 8, no. 2, pp. 158–172, 2023.
[3] A. Aastvedt and U. Higdem, “Co-creation, collaborative innovation and open innovation in the public sector: A perspective on distinctions and the convergence of definitions,” Nordic Journal of Innovation in the Public Sector, vol. 1, no. 1, pp. 53–68, Nov. 2022.
[4] S. V. Bhaskaran, “Automating and Optimizing Sarbanes-Oxley (SOX) Compliance in Modern Financial Systems for Efficiency, Security, and Regulatory Adherence,” International Journal of Social Analytics, vol. 7, no. 12, pp. 78–91, 2022.
[5] Y. Aiyar, Centre for Policy Research, Dharma Marg, Chanakyapuri, New Delhi, India, R. Alter, L. Bilmes, Hertie School, University of Governance, Berlin, Germany, and Kennedy School of Government, Harvard University, Cambridge, MA, United States, “Challenges of effective governance for sustainable development at subnational government levels: introduction to this thematic issue of Public Sector Economics,” Publ. Sect. Econ., vol. 46, no. 4, pp. 455–458, Dec. 2022.
[6] S. Rahman, M. R. M. Sirazy, R. Das, and R. S. Khan, “An Exploration of Artificial Intelligence Techniques for Optimizing Tax Compliance, Fraud Detection, and Revenue Collection in Modern Tax Administrations,” International Journal of Business Intelligence and Big Data Analytics, vol. 7, no. 3, pp. 56–80, 2024.
[7] M. Kandasamy, A. Sasikala, M. Sivaram, and V. Porkodi, “AI transformation in retail sectors,” Int. J. Public Sect. Perform. Manag., vol. 8, no. 3, p. 230, 2021.
[8] L. F. M. Navarro, “The Role of User Engagement Metrics in Developing Effective Cross-Platform Social Media Content Strategies to Drive Brand Loyalty,” Contemporary Issues in Behavioral and Social Sciences, vol. 3, no. 1, pp. 1–13, 2019.
[9] T. Boobier, AI and the future of the public sector. Nashville, TN: John Wiley & Sons, 2022.
[10] Y. Jani, “Real-time Anomaly Detection in Distributed Systems using Java and Apache Flink,” European Journal of Advances in Engineering and Technology, vol. 8, no. 2, pp. 113–116, 2021.
[11] L. J. Erasmus and S. B. Kahyaoğlu, Continuous auditing with AI in the public sector. Boca Raton: CRC Press, 2024.
[12] R. Das, M. R. M. Sirazy, R. S. Khan, and S. Rahman, “A Collaborative Intelligence (CI) Framework for Fraud Detection in U.S. Federal Relief Programs,” Applied Research in Artificial Intelligence and Cloud Computing, vol. 6, no. 9, pp. 47–59, 2023.
[13] M. R. M. Sirazy, R. S. Khan, R. Das, and S. Rahman, “Cybersecurity Challenges and Defense Strategies for Critical U.S. Infrastructure: A Sector-Specific and Cross-Sectoral Analysis,” International Journal of Information and Cybersecurity, vol. 7, no. 1, pp. 73–101, 2023.
[14] S. V. Bhaskaran, “Optimizing Metadata Management, Discovery, and Governance Across Organizational Data Resources Using Artificial Intelligence,” Eigenpub Review of Science and Technology, vol. 6, no. 1, pp. 166–185, 2022.
[15] M. Carr, “Public–private partnerships in national cyber-security strategies,” International Affairs, vol. 92, pp. 43–62, 2016.
[16] R. Khurana, “Implementing Encryption and Cybersecurity Strategies across Client, Communication, Response Generation, and Database Modules in E-Commerce Conversational AI Systems,” International Journal of Information and Cybersecurity, vol. 5, no. 5, pp. 1–22, 2021.
[17] A. I. Melo and L. F. Mota, “Public sector reform and the state of performance management in Portugal: is there a gap between performance measurement and its use?,” Int. J. Publ. Sect. Manag., vol. 33, no. 6/7, pp. 613–627, Oct. 2020.
[18] R. S. Khan, M. R. M. Sirazy, R. Das, and S. Rahman, “Data-Driven Perspectives on Federal Budgetary Dynamics for Identifying Anomalies and Patterns in Resource Allocation and Obligation Trends,” Quarterly Journal of Emerging Technologies and Innovations, vol. 9, no. 3, pp. 50–70, 2024.
[19] D. Kaul, “Optimizing Resource Allocation in Multi-Cloud Environments with Artificial Intelligence: Balancing Cost, Performance, and Security,” Journal of Big-Data Analytics and Cloud Computing, vol. 4, no. 5, pp. 26–50, 2019.
[20] F. Bartolacci, R. Del Gobbo, and M. Soverchia, “Improving public services’ performance measurement systems: applying data envelopment analysis in the big and open data context,” Int. J. Publ. Sect. Manag., Jul. 2024.
[21] H. Margetts and P. Dunleavy, “The political economy of digital government: How Silicon Valley firms drove conversion to data science and artificial intelligence in public management,” Public Money Manag., pp. 1–11, Aug. 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 author
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
