Cloud-Native Real-Time Analytics with Embedded Cyber Defense Using AI and Secure APIs
DOI:
https://doi.org/10.15662/IJEETR.2025.0706029Keywords:
Cloud-native, real-time analytics, artificial intelligence, cyber defense, secure APIs, anomaly detection, predictive analytics, cloud securityAbstract
In the era of exponential data growth, organizations increasingly rely on cloud-native solutions to process and analyze real-time data efficiently. Integrating artificial intelligence (AI) with secure application programming interfaces (APIs) enables organizations to not only gain actionable insights but also embed robust cyber defense mechanisms within their analytics pipelines. This study explores the design and implementation of cloud-native architectures that facilitate real-time analytics while simultaneously ensuring cybersecurity. By leveraging AI-driven threat detection, anomaly detection, and predictive analytics, cloud platforms can autonomously identify and mitigate potential security threats in real time. Secure APIs serve as the backbone of these systems, providing encrypted communication channels, authentication, and authorization mechanisms to prevent unauthorized access. The paper highlights current methodologies, implementation strategies, and challenges associated with integrating AI-based cyber defense into cloud-native analytics platforms. Additionally, it examines the advantages of scalability, cost-effectiveness, and rapid deployment alongside potential limitations, such as complexity and dependency on cloud providers. The research provides a holistic perspective on combining cloud computing, AI, and secure APIs to enhance both analytical capabilities and cybersecurity resilience. Practical recommendations and frameworks are proposed to guide future development in secure, intelligent, cloud-based analytics systems.
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