Machine Identities as the New Attack Surface: Securing API Keys, Service Accounts, Workloads, and Autonomous AI Agents in Zero Trust Environments
DOI:
https://doi.org/10.64235/wdb8xh84Keywords:
Machine Identity; Non-Human Identity; Zero Trust Security; API Security; Service Accounts; Workload Identity;Abstract
The rapid expansion of cloud computing, application programming interfaces, microservices, automated workloads, and autonomous AI agents has increased the number and complexity of machine identities operating within enterprise environments. API keys, service accounts, workload credentials, certificates, access tokens, and agent identities frequently possess privileged access to sensitive systems and data, making them attractive targets for credential theft, impersonation, privilege escalation, and lateral movement. Traditional identity and access management approaches remain largely centered on human users and are often inadequate for the scale, speed, and autonomy associated with machine-to-machine interactions. This study examines machine identities as an emerging cybersecurity attack surface and analyzes the security risks associated with API keys, service accounts, cloud workloads, CI/CD identities, and autonomous AI agents. It further evaluates the application of Zero Trust principles to machine authentication, authorization, credential management, continuous verification, and runtime monitoring. Based on threat modeling and security control analysis, the study proposes a Machine Identity Zero Trust Security Framework that integrates identity discovery, credential protection, strong authentication, least-privilege access, contextual authorization, continuous monitoring, automated revocation, and lifecycle governance. The framework provides a structured approach for reducing credential exposure, limiting privilege abuse, and improving accountability across modern enterprise and agent-based systems.
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