Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{179706,
        author = {N. BALASUBRAMANIAN and S. HARISHMA},
        title = {AUTHORIZATION DATA LEAKAGE DETECTION USING  HEURISTIC GUILT BASED ANALYSIS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7523-7527},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179706},
        abstract = {Data leakage poses a significant risk in environments where sensitive information is distributed among mul-tiple recipients. Traditional mechanisms like water-marking can be circumvented or are infeasible due to data integrity constraints. This paper proposes a Data Leakage Detection System utilizing a Heuristic Guilt-Based Analysis approach. The system embeds subtle, individualized data variations to track unauthorized disclosures and assess the likelihood of a leak originat-ing from specific agents. By analysing access patterns, behavioural deviations, and incorporating change point detection methods, the system effectively identifies guilty entities even in dynamic environments subject to concept drift. The model enhances traceability and accountability in data distribution, reducing the impact of insider threats and improving organizational data security practices.},
        keywords = {Data leakage detection, Heuristic guilt-based analysis, Insider threat, Concept drift, Process mining, Data security, Fingerprinting, Change detection.},
        month = {May},
        }