IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i1id704.html

AI-Driven Security Posture Management: A Revolutionary Approach to Multi-Cloud Enterprise Security

Author

Listed:
  • Srinivas Reddy Cheruku

Abstract

The landscape of cloud security has undergone a transformative evolution, driven by the complexity of modern digital infrastructures and the escalating sophistication of cyber threats. This comprehensive article explores an innovative AI-driven Cloud Security Posture Management (CSPM) framework that transcends traditional security methodologies. By leveraging advanced machine learning algorithms, neural network architectures, and intelligent automation, the framework offers a proactive, adaptive approach to cybersecurity that addresses the multifaceted challenges of multi-cloud environments. The article demonstrates how intelligent systems can dynamically analyze network interactions, predict potential vulnerabilities, and implement rapid, context-aware response mechanisms, fundamentally reshaping how organizations conceptualize and implement security strategies.

Suggested Citation

  • Srinivas Reddy Cheruku, 2025. "AI-Driven Security Posture Management: A Revolutionary Approach to Multi-Cloud Enterprise Security," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(1), pages 497-509, February.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:704
    DOI: 10.32628/CSEIT25111237
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25111237
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT25111237
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT25111237/CSEIT25111237
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT25111237?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:jbh:ijsrcs:v11:y2025:i1:id:704. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.