Open Access
Strategic Data Governance for Secure AI Adoption and Organizational Resilience: Addressing Challenges in SMEs and Large Enterprises
4
Independent Researcher, Data Governance & AI Security Frameworks, Bengaluru, India
Abstract
Background: In an era defined by the proliferation of Artificial Intelligence (AI) and big data, the
governance of information assets has transitioned from a back-office compliance function to a critical
strategic imperative. As organizations seek to leverage AI for competitive advantage, the quality,
security, and ethical management of underlying data have become paramount.
Methods: This study employs an integrative review of contemporary literature, industry frameworks
(such as DAMA-DMBOK), and empirical studies to analyze the evolving landscape of data governance.
The research specifically examines the distinct challenges faced by Small and Medium-sized Enterprises
(SMEs) compared to large corporations, alongside the critical role of governance in enabling secure AI
adoption.
Results: The analysis reveals that while foundational principles of governance remain consistent,
implementation strategies must be highly adaptive. Large enterprises often struggle with siloed data
and bureaucratic inertia, whereas SMEs face significant resource constraints that make comprehensive
frameworks difficult to operationalize. Furthermore, the integration of AI introduces complex ethical
variables, particularly regarding data privacy in sectors like healthcare and wearables.
Conclusion: Effective data governance requires a hybrid approach that balances rigid compliance with
agile decision-making. For AI to be secure and reliable, organizations must adopt metric-driven
governance models that prioritize data quality and ethical stewardship. The study suggests that future
frameworks must incorporate decentralized technologies, such as blockchain, to enhance trust and
transparency in multi-stakeholder environments.
Keywords
Data Governance,
Artificial Intelligence,
SMEs,
Data Ethics
References
Rajgopal, P. R., & Yadav, S. D. (2025). The role of data governance in enabling secure AI adoption. International Journal of Sustainability and Innovation in Engineering, 3, 1–25.
Sargiotis, D. (2024). Overcoming Challenges in Data Governance: Strategies for success. SpringerLink, 339–363.
Chu, D. (2024). How do data governance metrics enhance decision-making? Secoda.
Levstek, A., Pucihar, A., & Hovelja, T. (2022). Towards an Adaptive Strategic IT Governance Model for SMEs. J. Theor. Appl. Electron. Commerce Res., 17(1), 230–252.
West, C. (2023). SMEs and data governance: an approachable guide. Vaxa Analytics.
Prieto-Avalos, G., Cruz-Ramos, N.A., Alor-Hernández, G., Sánchez-Cervantes, J.L., Rodríguez-Mazahua, L., & Guarneros-Nolasco, L.R. (2022). Wearable Devices for Physical Monitoring of Heart: A Review. Biosensors, 12, 292.
Mangaroska, K., Martinez-Maldonado, R., Vesin, B., & Gaševi´c, D. (2021). Challenges and opportunities of multimodal data in human learning: The computer science students’ perspective. J. Comput. Assist. Learn., 37, 1030–1047.
Choudhury, S., Fishman, J.R., McGowan, M.L., & Juengst, E.T. (2014). Big data, open science and the brain: Lessons learned from genomics. Front. Hum. Neurosci., 8, 239.
DAMA International. (2024). DAMA-DMBOK Revised Edition, 2nd ed. Technics Publications: Denville, NJ, USA, 100–121.
Khatri, V., & Brown, C.V. (2010). Designing data governance. Commun. ACM, 53, 148–152.
McMurry, J.A., Juty, N., Blomberg, N., et al. (2017). Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data. PLoS Biol., 15, e2001414.
Ferretti, A., Ienca, M., Sheehan, M., et al. (2021). Ethics review of big data research: What should stay and what should be reformed? BMC Med. Ethics, 22, 51.
The DGI Data Governance Framework. (2020). The Data Governance Institute.
Feth, D., & Rauch, B. (2024). Datentreuhänder in der praxis. Datenschutz Und Datensicherheit - DuD, 48(2), 103–109.
Garcia, R.D., Ramachandran, G.S., Jurdak, R., & Ueyama, J. (2022). Blockchain-Aided and Privacy- Preserving data governance in Multi-Stakeholder applications. IEEE Trans Netw Serv Manage, 19(4), 3781–3793.
Georgiou, A., Magrabi, F., Hypponen, H., et al. (2018). The safe and effective use of shared data underpinned by stakeholder engagement and evaluation practice. Yearb Med Inform, 27(1), 25–28.
Goel, K., Emamjome, F., & Ter Hofstede, A.H.M. (2021). Data Governance for Managing Data Quality in Process Mining.
Hanapiah, N.M., Iahad, N.A., & Bahari, M. (2021). Identifying Principles and Ownership of Data Governance Framework for Higher Education Institution. Int. Conf. Res. Innov. Inf. Syst.
Hassani, H., & MacFeely, S. (2023). Driving excellence in official statistics: unleashing the potential of comprehensive digital data governance. Big Data Cogn Comput, 7(3), 134.
He, Q., Liu, Y., Jiang, L., Zhang, Z., Wu, M., & Zhao, M. (2023). Data sharing mechanism and strategy for Multi-Service integration for smart grid. Energies, 16(14), 5294.
Hildebrandt, K., Panse, F., Wilcke, N., & Ritter, N. (2020). Large-Scale Data Pollution with Apache Spark. IEEE Transactions on Big Data, 6(2), 396–411.
Huff, E., & Lee, J. (2020). Data as a Strategic Asset: Improving Results Through a Systematic Data Governance Framework. SPELACP.
Jang, K.-A., & Kim, W.-J. (2021). Development of data governance components using DEMATEL and content analysis. J Supercomputing, 77(4), 3695–3709.
Jayasinghe, U., Otebolaku, A., Um, T-W, & Lee, G.M. (2017). Data centric trust evaluation and prediction framework for IOT. 2017 ITU Kaleidoscope: Challenges for a Data-Driven Society.
CTG. (n.d.). Overcoming Common Data Governance Challenges. CTG Blog.
How to Cite
Dr. Pranav R. Kulshreshtha. (2025). Strategic Data Governance for Secure AI Adoption and Organizational Resilience: Addressing Challenges in SMEs and Large Enterprises . Global Multidisciplinary Journal, 4(11), 17-26. https://www.grpublishing.org/journals/index.php/gmj/article/view/197
Most read articles by the same author(s)
- Renuka Verma, IMPACT OF BRAND STIMULI ON SPENDING BEHAVIOR OF YOUTH IN COSMOPOLITAN CITIES OF NORTH INDIA , Global Multidisciplinary Journal: Vol. 3 No. 09 (2024): Volume 03 Issue 09
- Azeez Ahamed, THE INTERPLAY OF POLYMERS, PRECISION, AND SURFACE TOPOGRAPHY IN 3D PRINTING , Global Multidisciplinary Journal: Vol. 3 No. 10 (2024): Volume 03 Issue 10
- Hui Zhang, A FRAMEWORK FOR FUNCTIONAL PARTIALLY LINEAR SINGLE-INDEX MODELS: FORMULATION AND ANALYSIS , Global Multidisciplinary Journal: Vol. 2 No. 04 (2023): Volume 02 Issue 04
- Johnathan Mercer, Transforming Industries through Circular Economy and Industry 4.0: Integrative Business Model Innovation for Sustainable Value Creation , Global Multidisciplinary Journal: Vol. 4 No. 12 (2025): Volume 04 Issue 12
- Aymee Delfin, FEAR OF LOSS: EXPLORING CYNIC MENTAL CONTROL METHODS IN THE SANTIAGUEROS SCHOOL , Global Multidisciplinary Journal: Vol. 3 No. 06 (2024): Volume 03 Issue 06
- Dr. Amelia Torres, Transforming Merger and Acquisition Practice through Artificial Intelligence: A Theoretical and Applied Framework for AI-Enabled Due Diligence and Decision-Making , Global Multidisciplinary Journal: Vol. 4 No. 11 (2025): Volume 04 Issue 11
- Deepmala Jadhav, UNDERSTANDING NUTRITIONAL ANEMIA IN ADOLESCENT GIRLS: AN EPIDEMIOLOGICAL EXPLORATION , Global Multidisciplinary Journal: Vol. 3 No. 06 (2024): Volume 03 Issue 06
- Gregory Kokoszka, STATISTICAL INFERENCE FOR AUTOCOVARIANCE OF FUNCTIONAL TIME SERIES UNDER CONDITIONAL HETEROSCEDASTICITY , Global Multidisciplinary Journal: Vol. 1 No. 01 (2022): Volume 01 Issue 01
- Dr. Elena R. Vancroft, Dr. Marcus A. Thorne, Architectural Shifts in Modern Data Ecosystems: Evaluating the Symbiosis of Cloud Computing, Agile Data Modeling, and Business Intelligence for Competitive Advantage , Global Multidisciplinary Journal: Vol. 4 No. 10 (2025): Volume 04 Issue 10
- Dr. Pranav R. Kulshreshtha, Strategic Data Governance for Secure AI Adoption and Organizational Resilience: Addressing Challenges in SMEs and Large Enterprises , Global Multidisciplinary Journal: Vol. 4 No. 11 (2025): Volume 04 Issue 11
Similar Articles
- Owen B. Ashbourne, Automated Compliance and Governance in Cloud-Based Machine Learning Pipelines: Integrating MLOps, Auditability, and Regulatory Automation , Global Multidisciplinary Journal: Vol. 5 No. 02 (2026): Volume 05 Issue 02
- Dr. Gennarik L. Mortenkov, Synergizing Business Intelligence and Artificial Intelligence for Competitive Advantage: A Multi-Dimensional Analysis of Organizational Resilience and Decision-Making Frameworks , Global Multidisciplinary Journal: Vol. 4 No. 09 (2025): Volume 04 Issue 09
- Alexander P. Hofmann, Intelligent Governance Architectures for Regulated Digital States: Integrating Compliance, Risk, and Cybersecurity through Artificial Intelligence and Internet of Things Enabled Public Services , Global Multidisciplinary Journal: Vol. 4 No. 12 (2025): Volume 04 Issue 12
- Dr. Kristine Markovic, AI-Driven Decision Intelligence and Data-Centric Business Transformation: Reconfiguring Analytical Roles, Governance, And Cyber-Physical Ecosystems in The Age of Intelligent Automation , Global Multidisciplinary Journal: Vol. 5 No. 02 (2026): Volume 05 Issue 02
- Dr. Lukas Meyer, Integrating Hyperautomation, Generative Artificial Intelligence, and Intelligent Infrastructure for Smart Cities: A Unified Socio-Technical Framework , Global Multidisciplinary Journal: Vol. 5 No. 01 (2026): Volume 05 Issue 01
- Silas J. Merton, Integrating Artificial Intelligence and Real Time Data Processing in FinTech Credit Scoring Systems for Financial Inclusion and Risk Governance in Emerging Digital Economies , Global Multidisciplinary Journal: Vol. 4 No. 11 (2025): Volume 04 Issue 11
- Dr. Alejandro M. Torres, Artificial Intelligence–Enabled Financial Anomaly Detection and Reconciliation: Governance, Risk, and Explainability in Modern Accounting Ecosystems , Global Multidisciplinary Journal: Vol. 4 No. 08 (2025): Volume 04 Issue 08
- Dr. Lukas M. Verhoeven, Integrating Artificial Intelligence and Advanced Data Processing for Real-Time Credit Scoring: Theoretical Foundations, Methodological Innovations, and Implications for Contemporary Credit Risk Management , Global Multidisciplinary Journal: Vol. 4 No. 10 (2025): Volume 04 Issue 10
- Patrick L. Grayson, Behavioral Biometric Intelligence and Regulatory Convergence in Retirement Account Protection: An AI Driven Security Architecture for 401k Platforms , Global Multidisciplinary Journal: Vol. 4 No. 11 (2025): Volume 04 Issue 11
- Dr. Amelia Torres, Transforming Merger and Acquisition Practice through Artificial Intelligence: A Theoretical and Applied Framework for AI-Enabled Due Diligence and Decision-Making , Global Multidisciplinary Journal: Vol. 4 No. 11 (2025): Volume 04 Issue 11
1-10 of 122
Next
You may also start an advanced similarity search for this article.