AI-Driven Integration of Legacy IAM Services into AWS Cloud-Based Microservice Architectures: A Scalable Framework for Secure Identity Management

Most legacy IAM systems block cloud transformation initiatives due to a monolithic design, inflexibility, and many factors. Through this study, an AI based framework is offered that allows legacy IAM to be integrated with AWS microservice environment to increase scalability and security. The framework uses natural language processing (NLP) for policy extraction, graph neural networks for conflict resolution, and all the components are moved to AWS SageMaker for machine learning to provide an efficient, secure migration. Quantitative results confirm a 100ms rate of IAM operations with a 40% reduction of vulnerabilities and a 30% decrease in migration cost. It says that benefits of automation are highlighted, problems identifying interpretability challenges, and proposes future automation innovation through hybrid AI human oversight for complex migrations.

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Publication Details

Journal
International Journal of Innovative Research in Science Engineering and Technology
Published
2025-05-09
DOI
https://doi.org/10.15680/ijirset.2025.1405005
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Driven Integration of Legacy IAM Services into AWS Cloud-Based Microservice Architectures: A Scalable Framework for Secure Identity Management

Raja Mohan Dhanushkodi
International Journal of Innovative Research in Science Engineering and Technology
Software System Performance and Reliability
article

AI-Driven Integration of Legacy IAM Services into AWS Cloud-Based Microservice Architectures: A Scalable Framework for Secure Identity Management

Raja Mohan Dhanushkodi
article en

Abstract

Most legacy IAM systems block cloud transformation initiatives due to a monolithic design, inflexibility, and many factors. Through this study, an AI based framework is offered that allows legacy IAM to be integrated with AWS microservice environment to increase scalability and security. The framework uses natural language processing (NLP) for policy extraction, graph neural networks for conflict resolution, and all the components are moved to AWS SageMaker for machine learning to provide an efficient, secure migration. Quantitative results confirm a 100ms rate of IAM operations with a 40% reduction of vulnerabilities and a 30% decrease in migration cost. It says that benefits of automation are highlighted, problems identifying interpretability challenges, and proposes future automation innovation through hybrid AI human oversight for complex migrations.

International Journal of Innovative Research in Science Engineering and TechnologyVol. 14(05)
National Australia Bank (AU)
Openalex Percentile: Top 90%
Software System Performance and Reliability
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