Ensuring Data Security in Supply Chain Analytics: Emerging Issues and Solutions

DOI

10.2991/978-94-6239-616-6_116How to use a DOI?

Keywords

Supply Chain Analytics; Cybersecurity; Blockchain; Ransomware

Abstract

Supply Chain Analytics (SCA) has become a strategic enabler for optimizing procurement, logistics, and distribution activities in modern supply networks. As organizations increasingly depend on data-driven decision-making, the scale and sensitivity of data being generated, shared, and analysed have grown substantially. This expansion has intensified concerns related to data privacy, cybersecurity, and overall system integrity. Ensuring the confidentiality of analytical data, protecting against cyberattacks, and securing interconnected platforms have emerged as critical challenges. This paper examines the major security issues associated with supply chain analytics, including risks of data breaches, unauthorized access, manipulation of analytical outputs, and vulnerabilities arising from system integration across diverse digital platforms. It evaluates how cyber threats targeting analytical environments can disrupt operations, compromise decision accuracy, and weaken trust among supply chain partners.

The study explores advanced technological solutions to safeguard data flows within digital supply networks. These include blockchain-based architectures for secure and transparent data exchange, robust encryption protocols to protect sensitive information, and AI-driven anomaly detection systems capable of identifying unusual patterns and potential intrusions in real time. In addition, the paper proposes a strategic framework linking analytics maturity with data security resilience, emphasizing governance, continuous monitoring, and secure system design. By adopting these best practices, organizations can enhance trust, transparency, and operational continuity, ensuring resilient and secure data-driven supply chain ecosystems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - N. Manoharan
AU  - P. Loganathan
AU  - S. Prasanth
AU  - A. S. Thoufiq Nishath
PY  - 2026
DA  - 2026/03/31
TI  - Ensuring Data Security in Supply Chain Analytics: Emerging Issues and Solutions
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1643
EP  - 1652
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_116
DO  - 10.2991/978-94-6239-616-6_116
ID  - Manoharan2026
ER  -