AI Enabled Secure and Compliant Enterprise Data Platforms for Cross Cloud Analytics and Cybersecurity and Intelligent Automation
Abstract
Modern enterprises increasingly rely on cross-cloud environments to support data-driven decision-making, large-scale analytics, and intelligent automation across sectors such as finance, healthcare, retail, and digital services. However, distributing enterprise data across hybrid and multi-cloud infrastructures introduces complex challenges related to security, regulatory compliance, interoperability, and real-time governance. This study proposes an AI-enabled secure and compliant enterprise data platform designed to support cross-cloud analytics, cybersecurity monitoring, and intelligent automation within a unified architecture.
The proposed framework integrates layered security controls across application, network, infrastructure, and governance domains. Zero-trust access models, identity and access management, multi-factor authentication, and role-based controls ensure secure user and service interactions. Containerized microservices and service mesh technologies enable secure service-to-service communication, traffic encryption, and micro-segmentation across cloud environments. Data protection is strengthened through key management services, encryption at rest and in transit, runtime application self-protection, and immutable infrastructure strategies.
An embedded AI layer enables predictive analytics, anomaly detection, and automated threat response by leveraging enterprise telemetry, logs, and behavioral patterns. Cross-cloud data pipelines support real-time analytics and federated data processing while maintaining compliance with regulatory frameworks such as GDPR, HIPAA, and PCI-DSS. Intelligent automation components orchestrate deployment, policy enforcement, and remediation workflows, reducing operational complexity and improving resilience.
The architecture enhances enterprise visibility through centralized monitoring dashboards and governance engines that track compliance posture, risk exposure, and system performance across distributed environments. By combining AI-driven cybersecurity, secure cloud-native infrastructure, and automated governance, the proposed platform supports scalable, compliant, and resilient enterprise data operations. This approach enables organizations to safely harness cross-cloud analytics and intelligent automation while maintaining strong security and regulatory alignment in complex digital ecosystems
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 6 No. 4 (2023): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
10285-10293 |
Published |
August 10, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Rodrigo Emanuel Castro (%2023). AI Enabled Secure and Compliant Enterprise Data Platforms for Cross Cloud Analytics and Cybersecurity and Intelligent Automation. International Journal of Science, Research and Technology , Vol. 6 No. 4 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 10285-10293. https://doi.org/10.15662/IJSRAT.2023.0604002 |
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