DETECTING CLOUD SECURITY CONFIGURATION DRIFT BEFORE UNAUTHORIZED DATA EXPOSURE ACROSS MULTI-ACCOUNT ENTERPRISE INFRASTRUCTURE ENVIRONMENTS

Vanzal Dhliwayo

doi:10.5281/zenodo.22840247

Abstract

Unauthorized cloud data exposure increasingly originates not from the absence of security controls, but from
configuration states that gradually diverge from approved baselines as enterprise infrastructure changes. This
problem is amplified in multi-account environments where identity policies, storage permissions, security groups,
encryption settings, network routes, logging controls, and cross-account trust relationships evolve independently
across interconnected resources. This study proposes a preventive framework for detecting security configuration
drift before it forms an exploitable data-exposure pathway. Rather than treating every configuration difference as
equally significant, the framework reconstructs approved resource states and continuously evaluates observed
changes using deterministic policy validation, temporal persistence analysis, privilege-path assessment, and
multivariate anomaly detection. Drift events are correlated with data sensitivity, internet reachability, effective
permissions, cross-account access, resource criticality, and compensating-control status to calculate exposureoriented risk. High-risk trajectories including newly public storage, excessive IAM privilege, weakened
encryption, permissive network rules, and anomalous trust relationships are prioritized before unauthorized access
occurs. Policy-driven remediation, human approval for consequential changes, and post-remediation validation
establish a closed-loop control mechanism for reducing configuration-to-exposure time and strengthening
preventive security across distributed enterprise cloud estates.

Keywords

Configuration Drift; Cloud Security Posture; Multi-Account Infrastructure; Data Exposure Pathways; IAM

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