IDEAS home Printed from https://ideas.repec.org/a/alk/jcsts0/v7y2025i3id9683.html

AI-Powered Data Governance: Advancing Privacy and Compliance Through Data Masking and Sentiment Analysis

Author

Listed:
  • Bujjibabu Katta

    (Fidelity Investments, USA)

Abstract

The integration of artificial intelligence in data governance has revolutionized how organizations protect and manage sensitive information while ensuring regulatory compliance. Advanced data masking techniques combined with sentiment analysis capabilities have established new standards in privacy protection and compliance monitoring. The implementation of comprehensive frameworks incorporating static and dynamic data masking, coupled with real-time sentiment analysis, has significantly enhanced organizations' ability to detect and prevent compliance violations. Modern architectural considerations focusing on scalability, security, and resource optimization have enabled robust implementation of AI-driven governance solutions. The emergence of enhanced privacy technologies, including homomorphic encryption and federated learning systems, alongside explainable AI frameworks and edge computing integration, has transformed traditional data protection approaches. These technological advancements have led to substantial improvements in data security, compliance monitoring efficiency, and overall governance effectiveness while reducing operational complexities and associated costs.

Suggested Citation

  • Bujjibabu Katta, 2025. "AI-Powered Data Governance: Advancing Privacy and Compliance Through Data Masking and Sentiment Analysis," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(3), pages 837-844, April.
  • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:3:id:9683
    DOI: 10.32996/jcsts.2025.7.3.92
    as

    Download full text from publisher

    File URL: https://al-kindipublisher.com/index.php/jcsts/article/view/9683
    File Function: Abstract page
    Download Restriction: no

    File URL: https://al-kindipublisher.com/index.php/jcsts/article/download/9683/8326
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.32996/jcsts.2025.7.3.92?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:alk:jcsts0:v:7:y:2025:i:3:id:9683. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jcsts .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.