Designing Next-Generation Enterprise Systems with AI-Augmented Security Analytics and Cloud-Native Intelligence
DOI:
https://doi.org/10.15662/IJEETR.2023.0505011Keywords:
Artificial Intelligence, Enterprise Systems, Cloud-Native Architecture, Security Analytics, Machine Learning, Cybersecurity, Microservices, DevSecOps, Threat Detection, Operational IntelligenceAbstract
The rapid evolution of digital transformation has led enterprises to adopt cloud-native architectures and advanced analytics to remain competitive and resilient. However, the increasing complexity of enterprise systems introduces significant challenges in security, scalability, and operational efficiency. Artificial Intelligence (AI)-augmented security analytics has emerged as a critical enabler for designing next-generation enterprise systems that are intelligent, adaptive, and secure. This paper explores how AI-driven techniques can be integrated with cloud-native technologies to enhance threat detection, automate security responses, and optimize system performance. By leveraging machine learning, deep learning, and real-time data analytics, enterprises can proactively identify vulnerabilities, detect anomalies, and mitigate cyber threats. Additionally, cloud-native intelligence enables dynamic resource orchestration, microservices scalability, and continuous monitoring. The study examines current trends, architectural frameworks, and methodologies that combine AI and cloud-native principles for enterprise system design. It also highlights challenges such as data privacy, model explainability, and integration complexity. The findings demonstrate that AI-augmented security analytics, when combined with cloud-native intelligence, provides a robust foundation for building secure, scalable, and future-ready enterprise systems capable of adapting to evolving technological and cybersecurity landscapes.
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