The fastest-growing category of identities in your environment isn’t people—it’s systems. Non-human identities (NHIs) now perform the majority of actions in modern stacks. And with the rise of AI agents acting across services and teams, the problem that legacy access models face trying to keep up with modern realities is escalating fast. These identities don’t follow predictable workflows, don’t log into portals, and don’t wait for approvals. Yet most access models still treat them like people. That mismatch is fast becoming a critical blind spot.

When Identities Go Digital 

The transition from static to dynamic, automated infrastructure has led to an explosion of non-human identities (NHIs): service accounts, CI/CD pipelines, automation tools, ephemeral cloud resources, and increasingly, AI agents. These entities perform actions, access sensitive systems, and interact with critical workflows—often without direct human initiation or oversight.

Why NHIs Are a Different Class of Risk

This growth is reshaping the access landscape. Authorization decisions now apply not just to users, but to autonomous systems acting on their behalf. NHIs introduce new access patterns: high-frequency, high-variability, and often detached from predictable human workflows.

In many organizations, NHIs already outnumber humans—a trend accelerating with the rise of platform engineering, infrastructure-as-code, and embedded AI. Yet most access controls remain fundamentally human-centric, built around static roles, manual approvals, and long-lived credentials.

Why Legacy Access Can’t Handle It

Legacy models lack the context, precision, and policy architecture required to evaluate and contain NHI behavior at scale. Without continuous, scoped, and automated enforcement, access decisions drift from intent—and authorization becomes a liability, not a safeguard. 

Structural mismatches between traditional IAM and NHIs create persistent blind spots.

To avoid operational friction, NHIs are frequently overprovisioned and then forgotten due to unclear ownership or opaque dependencies. In systems with weak authorization boundaries, implicit trust becomes the default. Teams face an impossible tradeoff: leave access wide open and hope nothing breaks, or restrict it and risk disruption. Once granted, NHI access tends to persist—either because no one knows what it’s doing, or because removing it might break something critical.

Agent-Driven Complexity

LLM- or agent-driven workflows exacerbate this challenge. These agents:

• Act on behalf of multiple users or services

• Request access dynamically based on context or intent

• Operate across systems, chaining actions without human visibility

Securing agent-driven access using legacy role-based entitlements forces either overly permissive access or friction-inducing delays—undermining both productivity and security.

AI and Automation Demand a New Model

Authorization systems designed around human workflows cannot safely scale or adapt to the realities of NHIs (and particularly autonomous agents). NHIs behave differently: they’re often transient, lack clear ownership, and operate across high-frequency, unpredictable patterns. The challenge isn’t just scale—it’s volatility.

Traditional IAM models break down under this level of entropy. Without structural updates to support lifecycle ownership, scoped and time-bound access, and automated revocation, NHIs expand the attack surface beyond what legacy systems can control.

Want to learn more about how we got here, the real costs, and how organizations are gaining control? Download our ebook, “Identity Drift: How Authorization Became the Quiet Breach Vector”.