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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | AI-Powered Threat Detection and Policy-Based Segmentation in Multi-Tenant Cloud Infrastructure |
| Authors Name: | Praveen Kumar Thota |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_204687
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| Published Paper Id: | IJRTI2308122 |
| Published In: | Volume 8 Issue 8, August-2023 |
| DOI: | |
| Abstract: | Cloud service providers, while supporting multiple customers on a single platform, have found that standard security approaches are inadequate. These methods respond slowly and cannot keep up with rapidly evolving attack techniques. The paper examines a powerful approach that makes use of Artificial Intelligence (AI) for detecting threats and applying policies for separating various tenants to strengthen security in a multi-tenant cloud setting. The system uses automated anomaly detectors and neural networks working together with policies that segment workloads and access to services based on how risky or normal the systems are acting. It can learn from the network, web logs and telemetry information which allows it to find and stop known and unknown threats precisely. Also, through enforcement of zero-trust, segmentation policies prevent malicious actors from easily accessing multiple tenants. It has been proved by experiments that AI-based detection for security is superior to methods reliant on fixed rules in terms of how accurate, flexible and responsive it is to threats. That way, policies are used to increase security by changing access permissions on the fly which has a minimal effect on both loading speed and user experience. We stress that using AI can play a vital part in protecting the cloud and it is more important when dealing with complex cloud setups housing many tenants. Intelligent threat detection and adaptive segmentation both make threats visible and provide detailed control over how data and resources are accessed which helps to keep cloud security flexible and secure. Through this study, the body of autonomous cloud defense knowledge increases and opportunities are created for further study of using AI to handle security orchestration in cloud environments shared by many users |
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| Cite Article: | "AI-Powered Threat Detection and Policy-Based Segmentation in Multi-Tenant Cloud Infrastructure", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 8, page no.750-761, August-2023, Available :http://www.ijrti.org/papers/IJRTI2308122.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2308122
Registration ID:204687
Published In: Volume 8 Issue 8, August-2023
DOI (Digital Object Identifier):
Page No: 750-761 Country: Aldie, Virginia, United States Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2308122 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2308122 |
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ISSN: 2456-3315
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