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Integrating ai into enterprise java applications for secure high performance and scalable systems.

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Titel: Integrating ai into enterprise java applications for secure high performance and scalable systems.
Autoren: Jaiswal, Ishu Anand
Quelle: International Journal of Computational & Experimental Science & Engineering Experimental Science & Engineering (IJCESEN); 2025, Vol. 11 Issue 4, p7653-7662, 10p
Schlagwörter: ARTIFICIAL intelligence, PREDICTION models, MACHINE learning, JAVA programming language, COMPUTER architecture
Abstract: The integration of Artificial Intelligence (AI) into enterprise Java applications is rapidly emerging as a transformative approach to building intelligent, secure, and scalable systems. Traditional enterprise applications, though robust, often lack the adaptive capabilities required to handle modern workloads such as predictive analytics, anomaly detection, and intelligent automation. This paper explores a framework for embedding AI within enterprise Java environments by leveraging contemporary machine learning libraries, cloud-native deployments, and microservice architectures. Emphasis is placed on achieving high performance and scalability while addressing critical security challenges, including data privacy, model integrity, and secure inference. Through a proposed reference architecture and a case study implementation, the paper evaluates performance benchmarks, security considerations, and scalability trade-offs. The findings highlight that AI-enabled enterprise Java applications can provide significant improvements in system intelligence and efficiency, provided that integration is carefully designed with attention to performance optimization and security governance. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:The integration of Artificial Intelligence (AI) into enterprise Java applications is rapidly emerging as a transformative approach to building intelligent, secure, and scalable systems. Traditional enterprise applications, though robust, often lack the adaptive capabilities required to handle modern workloads such as predictive analytics, anomaly detection, and intelligent automation. This paper explores a framework for embedding AI within enterprise Java environments by leveraging contemporary machine learning libraries, cloud-native deployments, and microservice architectures. Emphasis is placed on achieving high performance and scalability while addressing critical security challenges, including data privacy, model integrity, and secure inference. Through a proposed reference architecture and a case study implementation, the paper evaluates performance benchmarks, security considerations, and scalability trade-offs. The findings highlight that AI-enabled enterprise Java applications can provide significant improvements in system intelligence and efficiency, provided that integration is carefully designed with attention to performance optimization and security governance. [ABSTRACT FROM AUTHOR]
ISSN:21499144
DOI:10.22399/ijcesen.4086