Machine Learning Powered Cloud-Native Platforms for Secure Enterprise API Workflows and Data Governance
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605016Keywords:
Machine Learning, Cloud-Native Architecture, API Security, Data Governance, Microservices, Cybersecurity, Anomaly Detection, Cloud Computing, Zero Trust, Enterprise SecurityAbstract
Machine learning powered cloud-native platforms are transforming the way enterprises secure API workflows and enforce data governance in distributed digital ecosystems. Modern enterprises rely heavily on microservices-based architectures where APIs act as the primary communication layer between services, applications, and external systems. This dependency increases exposure to security risks such as unauthorized access, data leakage, injection attacks, and abnormal API usage patterns. The proposed approach integrates machine learning models with cloud-native infrastructure to enable intelligent, adaptive, and automated security and governance mechanisms. By analyzing API logs, user behavior, and system telemetry in real time, machine learning algorithms can detect anomalies, predict potential threats, and enforce dynamic security policies. Cloud-native technologies such as containerization, orchestration platforms, and serverless computing provide scalability and flexibility, ensuring continuous monitoring and enforcement across large-scale systems. Additionally, automated data governance frameworks ensure compliance with regulatory standards by classifying, masking, and controlling sensitive data flows across APIs. The integration of machine learning with cloud-native API management significantly improves threat detection accuracy, reduces operational overhead, and enhances compliance assurance. This study highlights how intelligent automation and distributed cloud systems together create a resilient, scalable, and secure enterprise API ecosystem capable of adapting to evolving cybersecurity challenges
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