AI-Enabled Secure Enterprise Data Platforms for Predictive Analytics Fraud Detection Compliance Automation and Scalable Cloud Transformation
DOI:
https://doi.org/10.15662/IJEETR.2025.0703001Keywords:
Artificial Intelligence (AI), Predictive analytics, Fraud Detection, Compliance Automation, Cloud Transformation, Enterprise Data Platforms, Machine Learning, Cybersecurity, Data Governance, Distributed Computing, Cloud-Native Architecture, Real-Time Analytics, DevSecOps, Zero Trust Security, Data Engineering, Scalable Systems, Intelligent Automation, Enterprise Cloud, Risk Prediction, Digital TransformationAbstract
AI-enabled secure enterprise data platforms are transforming modern organizations by enabling intelligent decision-making, predictive analytics, fraud detection, compliance automation, and scalable cloud transformation. These platforms integrate cloud-native architectures, artificial intelligence (AI), machine learning (ML), distributed data engineering, and advanced cybersecurity mechanisms to process large-scale structured and unstructured enterprise data in real time. The framework emphasizes secure data ingestion, intelligent analytics pipelines, automated governance, privacy-preserving computation, and scalable cloud infrastructure to support financial systems, healthcare ecosystems, enterprise applications, and cyber defense environments
The proposed architecture combines AI-driven predictive analytics with fraud intelligence models, anomaly detection systems, and automated compliance monitoring to enhance operational resilience and reduce security risks. Technologies such as distributed data lakes, federated learning, zero-trust security, DevSecOps automation, and AI-powered governance engines enable secure and adaptive enterprise ecosystems. Furthermore, the framework supports scalable cloud transformation through containerized microservices, orchestration platforms, and real-time streaming analytics for high availability and business continuity
The study highlights the importance of integrating intelligent automation, explainable AI, data lineage management, and policy-driven governance into enterprise cloud platforms to improve transparency, scalability, and regulatory compliance. The proposed model delivers benefits including enhanced threat detection, predictive risk mitigation, intelligent resource optimization, reduced operational cost, and accelerated digital transformation. This research contributes toward the development of secure, scalable, and AI-driven enterprise data ecosystems capable of supporting next-generation predictive analytics and autonomous governance systems in dynamic cloud environments
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