Identity Lifecycle Management Wasn't Built for AI Agents

Identity lifecycle management was architected around a person with an employment record, a manager, and a departure date. AI agents have none of those. As autonomous principals proliferate across enterprise environments, the governance model built for humans develops structural blind spots that traditional IGA tools weren't designed to detect. This guide covers where that model breaks, what it

The article explains that traditional identity lifecycle and governance models were built for human employees with HR records, managers, and predictable joiner-mover-leaver events, but are misaligned with autonomous AI agents that lack these attributes. It highlights that as non-human, agentic identities proliferate, classic IGA and IAM controls develop blind spots around ownership, provisioning, monitoring, and decommissioning of these agents.[1][3][4] From a CyberSE.AI perspective, this creates a material compliance and governance risk: organizations must redefine identity policies, control frameworks, and oversight processes to treat AI agents as first-class, accountable identities, and to integrate them into lifecycle, access review, and deprovisioning workflows to avoid shadow agents, ungoverned privileges, and audit failures.[2][3][4]