Securing AI/ML Pipelines in the State Government Sector: Threat Models,  Controls, and Governance

Swapan Arora

Securing AI/ML Pipelines in the State Government Sector: Threat Models, Controls, and Governance

Authors

Swapan Arora

Keywords:

Artificial Intelligence Security, Machine Learning Pipelines, State Government Systems, Threat Modeling, Risk Management Framework, Secure MLOps

Abstract

Government authorities and public sector organizations increasingly deploy artificial intelligence andmachine learning systems in critical services including benefits eligibility verification, fraud detection,visitor flow optimization, and health monitoring. However, existing cybersecurity frameworks inadequately address vulnerabilities inherent in machine learning

References

Brent Daniel Mittelstadt et al., "The ethics of algorithms: Mapping the debate," Big Data & Society,

[Online]. Available: https://journals.sagepub.com/doi/pdf/10.1177/2053951716679679?source=post_page

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Published

2026-01-31

How to Cite

Swapan Arora. (2026). Securing AI/ML Pipelines in the State Government Sector: Threat Models, Controls, and Governance. Journal of Computational Analysis and Applications (JoCAAA), 35(1), 1178–1188. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4852