Permission Inheritance and Machine-Speed Risk Escalation in Agentic Al-Driven Cloud Operations

Abstract

This study investigates how permission inheritance and machine-speed execution influence cybersecurity risk escalation in agentic artificial intelligence–driven cloud environments. A quantitative empirical design was adopted using a network-based privilege propagation model and survival analysis. Identity and access relationships were represented as a directed authorization graph G=(V,E)G=(V,E)G=(V,E), while privilege expansion was evaluated using the Privilege Amplification Factor and Privilege Propagation Score derived from simulated cloud IAM datasets. Machine-speed escalation dynamics were examined through time-to-event modeling using survival probability and hazard ratio estimation. The findings reveal substantial privilege amplification, with administrative agents demonstrating an average Privilege Amplification Factor of 6.07 and access to 71% of reachable cloud resources. In addition, increasing execution speed from 5 to 200 operations per second reduced mean escalation time from 842 seconds to 68 seconds while raising the hazard ratio to 6.15. Based on these findings, the study proposes the Agentic Cloud Security Governance Model and recommends agent-specific identity isolation, dynamic least-privilege enforcement, real-time behavioral monitoring, and policy frameworks for governing autonomous agents in cloud infrastructures.