AI-Enabled Scalable Secure Cloud and Network Framework for Enterprise and Healthcare Data Platforms
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806817Keywords:
Scalable Cloud Security, AI-Driven Intrusion Detection, Data Lakehouse, API Governance, Enterprise Systems, Healthcare Platforms, Cloud ArchitectureAbstract
The increasing adoption of cloud platforms across enterprise and healthcare domains has amplified the need for scalable, secure, and intelligent data architectures capable of handling large volumes of sensitive information. Traditional cloud security mechanisms often struggle to provide real-time threat detection and governed data access in highly distributed environments. This paper presents a scalable secure cloud framework that integrates AI-driven intrusion detection systems (IDS) with an API-based data lakehouse to support enterprise and healthcare platforms. The proposed framework combines cloud-native scalability, intelligent network monitoring, and API governance to enable secure data ingestion, storage, and analytics while ensuring regulatory compliance and operational resilience. AI-driven IDS components continuously analyze network and system behavior to detect anomalies and potential attacks, while the API-governed data lakehouse enforces controlled data access, interoperability, and quality assurance. Experimental evaluation and architectural analysis demonstrate that the framework improves threat detection accuracy, enhances data governance, and supports high-throughput analytics, making it suitable for large-scale, security-sensitive enterprise and healthcare applications.
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