Automated Cloud Security Drift Detection: A Risk-Aware Framework

Abstract: Cloud environments are highly dynamic and continuously evolving, making them vulnerable to configuration drift, where resources deviate from their intended secure baseline settings. Such drift can occur due to manual changes, automated deployments, or misconfigured policies, leading to security risks such as excessive access permissions, exposed storage, and network vulnerabilities.

Most existing drift detection approaches focus on infrastructure consistency and lack key capabilities such as real-time monitoring, risk-based prioritization, and intent-aware analysis. Additionally, many solutions rely on periodic scanning, which is insufficient for modern cloud systems where changes occur rapidly.

To address these challenges, this paper proposes a Risk-Aware Automated Cloud Security Drift Detection Framework. The system uses event-driven audit logs to continuously monitor cloud environments, detect deviations from secure baselines, and classify them based on both risk level and intent. Based on this classification, high-risk misconfigurations are automatically remediated, while sensitive actions can be controlled through approval mechanisms.

The proposed framework is designed to be cloud-agnostic, enabling integration across major platforms such as AWS, Microsoft Azure, and Google Cloud Platform. This approach improves security visibility, reduces response time, and helps organizations maintain a stronger and more adaptive cloud security posture.

Keywords: Cloud Security, Configuration Drift, Identity and Access Management (IAM), Security Misconfigurations, Risk-Aware Detection, Automated Remediation, Event-Driven Monitoring, Multi-Cloud, Cybersecurity