Design and Validation of a Machine Identity Governance Framework for AI Agents in Multi-Cloud Environments

The rapid expansion of multi-cloud infrastructures and AI-driven automation has created a critical governance challenge: managing machine identities. Service accounts, APIs, and autonomous AI agents now handle sensitive data across AWS, Azure, and Google Cloud, yet traditional IAM systems cannot track or control these non-human entities effectively. This paper introduces the Machine Identity Governance Framework (MIGF), a unified model for monitoring, verifying, and governing machine identities in distributed cloud ecosystems. MIGF integrates a Lifecycle Governance Engine for automated identity lifecycle control, Autonomous Access Logging for tamper-evident audit trails, and Cross-Cloud Identity Mapping to harmonize credentials across providers. Tested on a real multi-cloud AI pipeline, MIGF reduced untracked service accounts by 52 %, improved audit response time by 41 %, and significantly enhanced lineage completeness. As machine identities are projected to vastly outnumber human ones by 2030, MIGF offers a scalable, policy-aligned solution for ensuring accountability, traceability, and security in AI-driven operations.