By Ido Shlomo, CTO and Co-Founder, Token Security
Security leaders have spent years hardening identity controls for employees and service accounts. That model is now showing its limits.
A new class of identity is rapidly spreading across enterprise environments, autonomous AI agents. Custom GPTs, copilots, coding agents running MCP servers, and purpose-built AI agents are no longer confined to experimentation. They are running and expanding in production, interacting with sensitive systems and infrastructure, invoking other agents, and making decisions and changes without direct human oversight.
Yet in most organizations, these agents exist almost entirely outside established identity governance. Traditional IAM, PAM, and IGA platforms were not designed for agents that are autonomous, decentralized, and adaptive. The result is a growing identity gap that introduces real security and compliance risk together with efficiency and effectiveness challenges.
Why AI Agents Break Existing Identity Models
Historically, enterprises managed two identity types: humans and machines. Identities whose goal is to serve human access are centrally governed, role-based, and relatively predictable. Machine and workload identities operate at scale but tend to be deterministic, repetitive, performing narrowly defined tasks.
AI agents fit neither and both categories at once.
They are goal-driven,and role-based, capable of adapting behavior based on intent and context, and able to chain actions across multiple systems. At the same time, they operate continuously and at machine speed and scale. This hybrid nature fundamentally alters the risk profile. AI agents inherit the intent-driven actions of human users while retaining the reach and persistence of machine identities.
Treating them as conventional non-human identities creates blind spots. Over-privileging becomes the default. Ownership becomes unclear. Behavior drifts from original intent. These are not theoretical concerns. They are the same conditions that have driven many identity-related breaches in the past, now amplified by autonomy and scale.
AI agents create, use, and rotate identities at machine speed—outpacing traditional IAM controls.
This guide shows CISOs how to manage the full lifecycle of AI agent identities, reduce risk, and maintain governance and audit readiness.
Download it free
Adoption Velocity without Security Is the Real Accelerator of Risk
What makes this challenge urgent is not just what AI agents are, but how quickly they are spreading.
Enterprises that believe they have just a few AI agents often discover hundreds or thousands once they look closely. Employees build custom GPTs. Developers spin up MCP servers locally. Business units integrate AI tools directly into workflows. Cleanup rarely happens.
Security teams are left unable to answer basic questions:
- How many AI agents exist?
- Who owns them?
- What systems, services, and data do they access?
- Which ones are still active?
This lack of visibility creates identity sprawl at machine speed. And as attackers have demonstrated repeatedly, abusing unmanaged credentials is often easier than exploiting software vulnerabilities.
The Case for AI Agent Identity Lifecycle Management
Identity risk accumulates over time. This is why organizations use joiner, mover, and leaver processes for its workforce and lifecycle controls for service accounts. AI agents experience the same dynamics, but compressed into minutes, hours or days.
AI Agents are created quickly, modified frequently, and often abandoned silently. Access persists. Ownership disappears. Quarterly access reviews and periodic certifications cannot keep pace.
AI Agent identity lifecycle management addresses this gap by treating AI agents as first-class identities governed continuously and near-real-time from creation through usage, ending up in decommissioning.
The goal is not to slow adoption, but to apply familiar identity principles, such as visibility, accountability, least privilege, and auditability, in a way that works for autonomous systems.
Download Token Security’s latest asset, an eBook designed to help you shape Lifecycle Management for your AI Agent identities from end to end.
Visibility Comes First: Discovering Shadow AI
Every identity control framework begins with discovery. Yet most AI agents never pass through formal provisioning or registration workflows. They run across cloud platforms, SaaS tools, developer environments, and local machines, making them invisible to traditional IAM systems.
From a Zero Trust perspective, this is a fundamental failure. An identity that cannot be seen cannot be governed, monitored, or audited. Shadow AI agents become unmonitored entry points into sensitive systems, often with broad permissions.
Effective discovery must be continuous and behavior-based. Quarterly scans and static inventories are insufficient when new agents can appear and disappear in a matter of minutes.
Ownership and Accountability Matters
One of the oldest identity risks is the orphaned account. AI agents dramatically increase both its frequency and impact.
AI agents are often created for narrow use cases or short-lived projects. When employees change roles or leave, or just grow tired of a certain AI product that hasn’t evolved, the agents they built frequently persist. Their credentials remain valid. Their permissions remain unchanged. No one remains accountable.
An autonomous agent without an owner can be perceived as a compromised identity. Lifecycle governance must enforce ownership and maintenance as a core requirement, flagging agents tied to departed users or inactive projects before they become liabilities.
Least Privilege Must Become Dynamic
AI agents are almost always over-privileged, not out of negligence, but uncertainty and the will to explore. Since their behavior can adapt, teams often grant broad access to avoid breaking workflows.
This approach is risky. An over-privileged agent can traverse systems faster than any human. In interconnected environments, a single agent can become the pivot point for widespread compromise or lateral movement.
Least privilege for AI agents cannot be static. It must be continuously adjusted based on observed behavior. Permissions that are unused should be revoked. Elevated access should be temporary and purpose-bound. Without this, least privilege remains a policy statement rather than an enforced control.
Traceability Is the Foundation of Trust
As enterprises move toward multi-agent systems, traditional logging models break down. Actions span agents, APIs, and platforms. Without correlated identity context, investigations and forensics or even compliance evidence become slow and incomplete.
Traceability is not just a forensic requirement. Regulators increasingly expect organizations to explain how automated systems make decisions, especially when those decisions affect customers or regulated data. Without identity-centric audit trails, that expectation cannot be met.
Identity Is Becoming the Control Plane for AI Security
AI agents are no longer emerging technology. They are becoming part of the enterprise operating model. As their autonomy grows, unmanaged identity becomes one of the largest sources of systemic risk.
AI Agent identity lifecycle management provides a pragmatic path forward. By treating AI agents as a distinct identity class and governing them continuously, organizations can regain control without stifling innovation.
In an agent-driven enterprise, identity is no longer just an access mechanism. It is becoming the control plane for AI security.
If you’d like more information on how Token Security is tackling AI security within the identity control pane, book a demo and we’ll show you how our platform operates.
Sponsored and written by Token Security.