Proactive Threat Intelligence and Detection Model Using Cloud-Native Security Tools

As enterprises increasingly migrate workloads to cloud platforms, the threat landscape has evolved with greater sophistication and velocity. Traditional reactive security models, reliant on post-incident response, are insufficient to address modern attack vectors such as identity compromise, misconfigured cloud services, and lateral movement within hybrid environments. This study proposes a Proactive Threat Intelligence and Detection Model leveraging cloud-native security tools to provide real-time monitoring, early threat identification, and automated mitigation. The model integrates threat intelligence feeds, machine learning, and anomaly detection to deliver predictive insights, enabling organizations to act before threats materialize into security incidents. The model begins with a data collection phase, centralizing telemetry from cloud workloads, applications, network logs, and identity services. External threat intelligence feeds augment internal data, enriching the context for detection. In the analysis phase, AI-driven correlation and behavioral analytics identify deviations from normal activity, flagging potential indicators of compromise (IoCs) and suspicious patterns. Risk scoring and prioritization mechanisms allow security teams to focus on high-impact threats, reducing alert fatigue and optimizing resource allocation. The response phase incorporates automated remediation, such as account suspension, workload isolation, or conditional access enforcement, while retaining human-in-the-loop oversight for complex scenarios. Continuous monitoring and feedback loops refine detection algorithms over time, adapting to evolving threats and organizational changes. By leveraging cloud-native security platforms—such as Microsoft Defender, AWS GuardDuty, or Google Chronicle—the model provides scalable, integrated, and real-time security coverage across IaaS, PaaS, and SaaS environments. This proactive approach enhances enterprise resilience by shifting from reactive incident response to predictive threat management. The proposed model ensures early detection, minimizes operational disruption, and supports compliance with regulatory standards. Ultimately, it positions organizations to leverage cloud-native capabilities, integrate AI-driven threat intelligence, and maintain robust, adaptive, and proactive cybersecurity frameworks in complex, hybrid, and multi-cloud environments.