Cloud Native Machine Learning Framework for Automated Predictive Enterprise Risk Assessment and Security Intelligence

Abstract

The rapid digital transformation of enterprises has generated increasingly complex technology ecosystems involving
cloud platforms, microservices, containers, application programming interfaces, distributed databases, remote endpoints,
and third-party services. This complexity creates new operational, financial, compliance, and cybersecurity risks that
conventional risk-assessment approaches may not identify rapidly enough. This study proposes a cloud-native machine
learning framework for automated predictive enterprise risk assessment and security intelligence. The framework integrates
heterogeneous enterprise data, including security logs, operational events, user behavior, vulnerability information,
financial indicators, compliance records, and threat-intelligence feeds, to develop a unified representation of organizational
risk. Machine learning algorithms are employed to identify hidden patterns, detect anomalies, predict
emerging risks, and classify security events according to their potential impact. A cloud-native architecture based on
containerized services, scalable data pipelines, distributed processing, model-serving components, and continuous monitoring
enables the framework to operate across dynamic enterprise environments. The proposed methodology incorporates
data preprocessing, feature engineering, predictive model development, risk scoring, explainable artificial intelligence,
automated alert prioritization, and continuous model updating. Model performance is evaluated using accuracy,
precision, recall, F1-score, area under the ROC curve, false-positive rate, prediction latency, scalability, and resource utilization.
The framework aims to transform enterprise risk management from periodic and reactive assessment into continuous,
intelligent, predictive, and automated security intelligence capable of supporting timely organizational decisionmaking.