An Intelligent Cloud-Native Framework for Continuous Enterprise Risk Prediction and Automated Governance
DOI:
https://doi.org/10.21590/Keywords:
Cloud-Native Governance, Continuous Risk Prediction, Machine Learning, Policy-as-Code, Automated Remediation, Enterprise Risk Management.Abstract
Modern enterprises operate in highly dynamic, distributed environments where traditional, periodic risk assessment
models fail to address rapid threat evolution. This paper proposes an Intelligent Cloud-Native Framework for Continuous
Enterprise Risk Prediction and Automated Governance. The framework leverages distributed stream processing, machine
learning (ML), and infrastructure-as-code (IaC) mutations to detect, predict, and remediate compliance and security risks
in real time. By integrating graph-based anomaly detection with predictive risk scoring, the system forecasts potential
governance failures before they impact the organization. The architecture utilizes a microservices-driven approach,
employing automated policy-as-code engines to trigger real-time, self-healing governance workflows. Evaluation results
demonstrate that the framework achieves a 94% reduction in risk mitigation latency and improves predictive accuracy
by 38% compared to legacy batch-processing governance models. Ultimately, this research provides a scalable, resilient
blueprint for modern organizations aiming to transition from reactive compliance postures to proactive, autonomous risk
orchestration in cloud-native ecosystems.
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