Data Poisoning, Data Drift, and Data Integrity in Supply Chain Systems
Emerging Threats to AI Governance
DOI:
https://doi.org/10.55537/spk.v5i1.1601Keywords:
AI Governance, Data Poisoning, Data Drift, Data Integrity, Supply Chain Systems, Ethical AIAbstract
Artificial intelligence (AI) plays a critical role in supply chain systems by enabling predictive analytics and data-driven decision-making. However, increasing reliance on AI exposes systems to significant data-related vulnerabilities that may compromise reliability and trust. This study investigates three major threats to AI governance: data poisoning, data drift, and data integrity. Using a qualitative literature-based analysis supported by synthesized empirical evidence, this study evaluates the impact of these threats on model performance and operational outcomes. The results show that data poisoning can significantly reduce model accuracy (from approximately 95% to below 75%) and introduce bias, while data drift leads to gradual performance degradation over time due to changing data distributions. In addition, data integrity issues—such as incomplete, corrupted, or unauthorized data—undermine decision reliability and amplify the effects of poisoning and drift. To address these challenges, the study proposes a multi-layered AI governance framework integrating technical safeguards (e.g., adversarial detection and continuous monitoring), organizational controls, and policy-level compliance mechanisms. The findings provide practical insights for improving AI robustness, operational resilience, and trust in supply chain environments, contributing to the development of effective and responsible AI governance.
Downloads
References
C. Cath, “Governing artificial intelligence: Ethical, legal and technical opportunities and challenges,” Philosophical Transactions of the Royal Society A, vol. 376, no. 2133, p. 20180080, 2018.
U. Gasser and V. A. Almeida, “A layered model for AI governance,” IEEE Internet Computing, vol. 21, no. 6, pp. 58–62, 2017, doi: 10.1109/MIC.2017.4180835.
J. Fjeld, N. Achten, H. Hilligoss, A. Nagy, and M. Srikumar, “Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI,” SSRN Electronic Journal, 2020, doi: 10.2139/ssrn.3518482.
J. Morley, L. Murphy, A. Mishra, I. Joshi, and K. Karpathakis, “Governing data and artificial intelligence for health care: Developing an international understanding,” JMIR Formative Research, vol. 6, no. 1, p. e31623, 2022, doi: 10.2196/31623.
C. Lahusen, M. Maggetti, and M. Slavkovik, “Trust, trustworthiness and AI governance,” Scientific Reports, vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-71761-0.
K. Yeung, A. Howes, and G. Pogrebna, “AI governance by human rights-centred design, deliberation and oversight: An end to ethics washing,” SSRN Electronic Journal, 2019, doi: 10.2139/ssrn.3435011.
M. Aljanabi et al., “Data poisoning: Issues, challenges, and needs,” in IET Conference Proceedings, 2024, doi: 10.1049/ICP.2024.0951.
K. I. Iyer, “Poisoning AI models: New frontiers in data manipulation attacks,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 11, no. 11, 2023.
N. Lekota, “Governance considerations of adversarial attacks on AI systems,” in Proc. International Conference on AI Research, vol. 4, no. 1, pp. 227–233, 2024, doi: 10.34190/ICAIR.4.1.3194.
H. Zhang et al., “Practical data poisoning attack against next-item recommendation,” in Proc. The Web Conference, 2020, pp. 2458–2464, doi: 10.1145/3366423.3379992.
A. E. Cinà et al., “Wild patterns reloaded: A survey of machine learning security against training data poisoning,” ACM Computing Surveys, vol. 55, no. 13s, pp. 1–39, 2023.
J. Hausenloy, D. McClements, and M. Thakur, “Towards data governance of frontier AI models,” arXiv preprint, 2024.
P. Mahendra et al., “A comprehensive review of AI and ML in data governance and data quality,” in Proc. ICICI, 2025, pp. 1–6.
E. Bluemke, T. Collins, B. Garfinkel, and A. Trask, “Exploring the relevance of data privacy-enhancing technologies for AI governance use cases,” arXiv preprint, 2023.
S. V. Bayani, S. Prakash, and L. Shanmugam, “Data guardianship: Safeguarding compliance in AI/ML cloud ecosystems,” Journal of Knowledge Learning and Science Technology, vol. 2, no. 3, pp. 436–456, 2023, doi: 10.60087/jklst.vol2.n3.p456.
M. M. Rahman et al., “Security risk and attacks in AI: A survey of security and privacy,” in Proc. IEEE COMPSAC, 2023, pp. 1834–1839, doi: 10.1109/COMPSAC57700.2023.00284.
M. T. Hossain et al., “A review on attacks against artificial intelligence and their defence,” Control Systems and Optimization Letters, vol. 2, no. 1, pp. 52–59, 2024, doi: 10.59247/csol.v2i1.73.
M. K. Pasupuleti, “Securing AI-driven infrastructure: Advanced cybersecurity frameworks for cloud and edge computing environments,” 2025, doi: 10.62311/nesx/rrv225.
A. K. Sharma and R. Sharma, “Data governance in the age of artificial intelligence: Challenges, best practices and regulatory compliance,” Applied Marketing Analytics, vol. 10, no. 4, p. 390, 2025, doi: 10.69554/xwhm1191.
I. M. Leghemo et al., “Data governance for emerging technologies: A conceptual framework for managing blockchain, IoT, and AI,” Journal of Engineering Research and Reports, vol. 27, no. 1, pp. 247–267, 2025, doi: 10.9734/jerr/2025/v27i11385.
K. Elliott et al., “Towards an equitable digital society: AI and corporate digital responsibility,” Society, vol. 58, no. 3, pp. 179–188, 2021, doi: 10.1007/s12115-021-00594-8.
N. N. Gupta, “How inadequate data governance frameworks lead to unethical outcomes in artificial intelligence systems,” International Journal of Scientific Research Archive, vol. 7, no. 1, pp. 580–590, 2022, doi: 10.30574/ijsra.2022.7.1.0274.
J. Woodhams, “Maintaining research integrity when using AI in research,” 2025.
R. Nacheva and O. Azeroual, “Security of AI-powered systems: Threat intelligence on the edge,” in Proc. ISMSIT, 2024, doi: 10.1109/ISMSIT63511.2024.10757185.
R. S. T. Palanichamy, “AI and data governance: Enhancing security, privacy, and accountability,” International Journal of Science and Advanced Technology, vol. 14, no. 1, 2023, doi: 10.71097/ijsat.v14.i1.2807.
D. Chhillar and R. V. Aguilera, “An eye for artificial intelligence: Insights into the governance of AI and vision for future research,” Business & Society, vol. 61, no. 5, pp. 1197–1241, 2022, doi: 10.1177/00076503221080959.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Umamaheswari Shanmugam; Mohan K Rajendran, Jawahar Natarajan, Satyanarayana Reddy
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
