ML-Based Data Leakage and Tampering Detection System in Enterprise Data Workflows

DOI

10.2991/978-94-6239-616-6_7How to use a DOI?

Keywords

Enterprise Security; Machine Learning; Insider Threats; Data Leakage; Behavioral Analytics; Document Integrity; Explainable AI

Abstract

Modern enterprises face escalating risks of insider threats, data leakage, and document tampering as digital workflows grow increasingly complex. Conventional rule-based security tools fail to detect adaptive, authorized-user attacks that exploit behavioral and semantic anomalies. This study presents a comprehensive survey and conceptual framework integrating behavioral analytics, automated content categorization, and document integrity verification using advanced machine learning models such as Isolation Forests for anomaly detection and transformer-based language models for semantic analysis. The proposed multi-modal framework emphasizes transparency, explainability, and adaptability across enterprise environments. A simulated evaluation using synthetic enterprise datasets demonstrates detection accuracy of 96.2%, a false positive rate of 2.0%, and scalability across varied workloads—showing the framework’s feasibility for real-time, ML-driven data protection. The paper also identifies key research gaps and outlines future directions toward privacy-preserving, federated, and explainable AI-based enterprise security architectures.

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  - R. Anandkumar
AU  - M. Thamimul Ansari
AU  - M. Naraen
AU  - A. A. Shiva Parvathan
PY  - 2026
DA  - 2026/03/31
TI  - ML-Based Data Leakage and Tampering Detection System in Enterprise Data Workflows
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 81
EP  - 96
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_7
DO  - 10.2991/978-94-6239-616-6_7
ID  - Anandkumar2026
ER  -