Exploring AI-Driven Approaches for Safeguarding Sensitive ERP, HR, and Defense Data within U.S. Organizations

Abstract

In the era of digital transformation, safeguarding sensitive data in systems like ERP, HR, and defense platforms is a critical priority for U.S. institutions. Traditional cybersecurity often fails against evolving threats, but Artificial Intelligence (AI) offers a proactive, intelligent solution for real-time risk detection and mitigation. This study investigates how AI-driven strategies, data protection mechanisms, and supportive organizational policies collectively enhance data security and resilience.

A quantitative study surveyed 300 U.S. professionals across private, public, defense, and research sectors. Data analysis revealed strong positive perceptions of AI-driven security (mean ≈ 4.0). Statistical analysis confirmed significant positive relationships between all key variables. Regression identified Data Protection Mechanisms (β = 0.43) as the strongest predictor of AI's perceived effectiveness, followed by AI-Based Security Strategies (β = 0.31) and Organizational Policies (β = 0.25). Furthermore, significant differences existed across sectors, with the defense sector rating AI effectiveness highest.

The findings confirm that AI significantly enhances data protection and organizational resilience. Its success is strongly influenced by integrated data mechanisms, robust AI strategies, and supportive policies. The defense sector's higher perception underscores the value of structured AI adoption in high-stakes environments. Organizations should invest in AI-enhanced security systems, sophisticated data protection, and align with national cybersecurity regulations. Leadership commitment, ethical AI governance, and comprehensive employee training are crucial for maximizing benefits. Cross-sector collaboration and continuous innovation are recommended to maintain security performance and ensure equitable AI implementation across all organizations.

References

Adedeji, A. (2024). An Examination of Contemporary Conflict Management Approach in the 21st Century Nigeria. Inverge Journal of Social Sciences, 3(4), 32–44. https://doi.org/10.63544/ijss.v3i4.98

Aiswarya, R. S. (2021). Cloud Infrastructure Security Using AI-Powered Threat Prediction and Mitigation. Journal of Techno Social, 13(1).

Alami, H., Lehoux, P., Denis, J. L., Motulsky, A., Petitgand, C., Savoldelli, M., ... & Fortin, J. P. (2021). Organizational readiness for artificial intelligence in health care: insights for decision-making and practice. Journal of Health Organization and Management, 35(1), 106-114.

Asif, M. (2022). Integration of Information Technology in Financial Services and its Adoption by the Financial Sector in Pakistan. Inverge Journal of Social Sciences, 1(2), 23-35.

Asif, D. M. (2024). THE COMPLEXITIES OF BIOTERRORISM: CHALLENGES AND CONSIDERATIONS. International Journal of Contemporary Issues in Social Sciences, 3(3), 2175-2184.

Aurangzeb, D., & Asif, M. (2021). Role of leadership in digital transformation: A case of Pakistani SMEs. In Fourth International Conference on Emerging Trends in Engineering, Management and Sciences (ICETEMS-2021)(4 (1), 219-229).

Aurangzeb, M., Tunio, M., Rehman, Z., & Asif, M. (2021). Influence of administrative expertise on human resources practitioners on the job performance: Mediating role of achievement motivation. International Journal of Management, 12(4), 408-421.

Bawa, S. S. (2020). Automate enterprise resource planning with bots. Int. J. Comput. Trends Technol, 68(4), 1-5.

Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080.

Cheng, X., Su, L., Luo, X., Benitez, J., & Cai, S. (2022). The good, the bad, and the ugly: Impact of analytics and artificial intelligence-enabled personal information collection on privacy and participation in ridesharing. European Journal of Information Systems, 31(3), 339-363.

Chinta, P. C. R. (2020). A Deep Learning Architectures for Enhancing Cyber Security Protocols in Big Data Integrated ERP Systems. Journal of Artificial Intelligence and Big Data, 1(1), 10-31586.

Dalal, A. (2019). AI Powered Threat Hunting in SAP and ERP Environments: Proactive Approaches to Cyber Defense. Available at SSRN 5198746.

Das, N., Hossain, M. E., Ahmed, N., Rimon, S. T. H., & Hasib, M. F. S. (2022). Harnessing AI Driven Predictive Maintenance: Transforming Manufacturing Efficiency and Reducing Downtime through Advanced Data Analytics. Propel Journal of Academic Research, 2(2), 299-328.

Esan, O. J., Uzozie, O. T., Onaghinor, O., Osho, G. O., & Etukudoh, E. A. (2022). Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework. J. Front. Multidiscip. Res, 3(1), 117-123.

Ezeife, E., Kokogho, E., Odio, P. E., & Adeyanju, M. O. (2021). The future of tax technology in the United States: A conceptual framework for AI-driven tax transformation. Future, 2(1), 101203.

Faruk, O. M., & Khan, M. K. (2022). BLOCKCHAIN-ENABLED BI FOR HR AND PAYROLL SYSTEMS: SECURING SENSITIVE WORKFORCE DATA. American Journal of Scholarly Research and Innovation, 1(02), 30-58.

Galla, E. P., Rajaram, S. K., Patra, G. K., Madhavram, C., & Rao, J. (2022). AI-driven threat detection: Leveraging big data for advanced cybersecurity compliance. Available at SSRN 4980649.

Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial intelligence in healthcare (pp. 295-336). Academic Press.

Hu, Y., Kuang, W., Qin, Z., Li, K., Zhang, J., Gao, Y., ... & Li, K. (2021). Artificial intelligence security: Threats and countermeasures. ACM Computing Surveys (CSUR), 55(1), 1-36.

Imran, M., Khan, A., Anderson, J., & Gonzalez, M. (2022). Artificial Intelligence and Machine Learning Applications in ICT: Transforming Industry Practices. International Journal of Information and Communication Technology Trends, 2(1), 118-129.

Inyang, U., G. Etuk, S., & Effiom, M. (2024). Employees’ Assessment of Impact of Information Systems on Operational Efficiency of Insurance Companies. Inverge Journal of Social Sciences, 3(3), 1–12. https://doi.org/10.63544/ijss.v3i3.86

Ishii, K. (2019). Comparative legal study on privacy and personal data protection for robots equipped with artificial intelligence: looking at functional and technological aspects. AI & society, 34(3), 509-533.

Johnson, J. (2019). Artificial intelligence & future warfare: implications for international security. Defense & Security Analysis, 35(2), 147-169.

Khan, M. N. I. (2022). A Systematic Review of Legal Technology Adoption In Contract Management, Data Governance, And Compliance Monitoring. American Journal of Interdisciplinary Studies, 3(01), 01-30.

Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135-155.

Mehr, H., Ash, H., & Fellow, D. (2017). Artificial intelligence for citizen services and government. Ash Cent. Democr. Gov. Innov. Harvard Kennedy Sch., no. August, 1, 12.

Muntala, P. S. R. P. (2022). Detecting and Preventing Fraud in Oracle Cloud ERP Financials with Machine Learning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 57-67.

Nuka, S. T. (2022). The role of AI driven clinical research in medical device development: A data driven approach to regulatory compliance and quality assurance. Global Journal of Medical Case Reports, 2(1), 1275.

Onoja, J. P., Hamza, O., Collins, A., Chibunna, U. B., Eweja, A., & Daraojimba, A. I. (2021). Digital transformation and data governance: Strategies for regulatory compliance and secure AI-driven business operations. J. Front. Multidiscip. Res, 2(1), 43-55.

Owobu, W. O., Abieba, O. A., Gbenle, P., Onoja, J. P., Daraojimba, A. I., Adepoju, A. H., & Ubamadu, B. C. (2021). Review of enterprise communication security architectures for improving confidentiality, integrity, and availability in digital workflows. IRE Journals, 5(5), 370-372.

Pandey, B. K., Tanikonda, A., Peddinti, S. R., & Katragadda, S. R. (2021). Ai-driven methodologies for mitigating technical debt in legacy systems. Journal of Science & Technology (JST), 2(2).

Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.

Raso, F. A., Hilligoss, H., Krishnamurthy, V., Bavitz, C., & Kim, L. (2018). Artificial intelligence & human rights: Opportunities & risks. Berkman Klein Center Research Publication, (2018-6).

Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American medical informatics association, 27(3), 491-497.

Routhu, K., Bodepudi, V., Jha, K. M., & Chinta, P. C. R. (2020). A Deep Learning Architectures for Enhancing Cyber Security Protocols in Big Data Integrated ERP Systems. Available at SSRN 5102662.

Shneiderman, B. (2020). Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4), 1-31.

Sriram, H. K. (2022). AI Neural Networks In Credit Risk Assessment: Redefining Consumer Credit Monitoring And Fraud Protection Through Generative AI Techniques. Migration Letters, 19(6), 1017-1032.

Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., ... & Teller, A. (2022). Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence. arXiv preprint arXiv:2211.06318.

Trunk, A., Birkel, H., & Hartmann, E. (2020). On the current state of combining human and artificial intelligence for strategic organizational decision making. Business Research, 13(3), 875-919.

Varian, H. (2018). Artificial intelligence, economics, and industrial organization. In The economics of artificial intelligence: an agenda (pp. 399-419). University of Chicago Press.

Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business process management journal, 26(7), 1893-1924.

Zehir, C., Karaboğa, T., & Başar, D. (2019). The transformation of human resource management and its impact on overall business performance: big data analytics and AI technologies in strategic HRM. In Digital business strategies in blockchain ecosystems: Transformational design and future of global business (pp. 265-279). Cham: Springer International Publishing.

Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN 3312874.