Organizations are not struggling to adopt AI. They are struggling to secure it.
Across all industries, the push toward
agentic AI is accelerating. Business leaders see automation as a competitive necessity, and teams are moving quickly to deploy AI agents across workflows, infrastructure, and decision-making systems. But in that race for speed, a critical control layer is being left behind:
identity governance.
The result of this oversight is not just increased risk. It is systemic
exposure.
Delinea's
2026 Identity Security Report highlights a growing disconnect between confidence and capability. While 87% of organizations think they're ready to support AI-driven automation, nearly half admit that their identity governance controls are insufficient for these environments.
This confidence paradox reflects a deeper problem. Organizations believe they are secure but lack the visibility and control to validate that assumption in real-time.
Bridging the visibility gap
At the core of this challenge is identity fragmentation.
Beyond human users, organizations must manage
non-human identities (NHIs), including service accounts, machine identities, and most recently autonomous AI agents.
These identities operate across
cloud platforms, APIs, and internal systems, often with elevated privileges and minimal oversight. Yet most organizations cannot fully see them.
According to Delinea's report, 90% of organizations lack total visibility into the activities, access and even existence of non-human identities, particularly in AI-driven environments. This visibility gap makes it hard to spot anomalous behavior, enforce
governance, or even understand who or what has access to critical systems.
This is where the concept of an identity control plane becomes essential. It's not just another enforcement tool, but rather a governance layer that continuously discovers, monitors, and validates identity activity across the environment.
It supplies a unified view of human, machine, and AI-driven identities, enabling organizations to understand access relationships,
detect risk, and enforce policies consistently. Without this layer, identity will become the weakest link in AI adoption.
A sprawling, tangled web
The risks are already forming. As AI adoption increases, so does identity sprawl. AI agents create new access paths, often interacting with systems in non-deterministic ways. Unlike traditional NHIs, which have fixed tasks and abilities, AI agents can request additional privileges, access new systems, and take actions that are not explicitly predefined.
In many cases, organizations are granting these agents persistent, high-level access to ensure performance and uptime. The Delinea report shows that 73% of organizations acknowledge that maintaining standing resource access for AI agents increases risk, yet 74% say it is necessary to meet operational demands. To move quickly and stay competitive, organizations are expanding trust faster than they can govern it.
Attackers are taking advantage of this contradiction. Identity has become the primary attack surface in modern environments. Rather than exploiting vulnerabilities, attackers increasingly use stolen credentials or session tokens to penetrate systems, move laterally and escalate privileges.
In AI-driven systems, where identities may be numerous, dynamic, and often poorly governed, this attack surface can expand dramatically.
Hiding in the shadows
Organizations are not just dealing with more identities. They are dealing with identities they cannot fully account for.
This is compounded by the rise of shadow AI. More than half of organizations report encountering unauthorized AI tools accessing company systems, yet only a small percentage of companies can detect this activity in real time. These unsanctioned tools often operate with legitimate user credentials, making them difficult to distinguish from normal activity.
The result is a growing blind spot. To address this, organizations must move toward what can best be described as machine-speed identity security.
Traditional identity-governance models, built around human users and periodic reviews, cannot keep pace with AI-driven environments. Instead, organizations need continuous discovery, real-time monitoring, and dynamic access controls that align with zero-trust principles.
This includes moving away from static, long-lived credentials toward just-in-time (JIT) and ephemeral access models, as well as enforcing least privilege not just for users, but for autonomous systems.
None of this is possible without visibility. The first step in closing the identity gap is understanding the full scope of identities operating within the environment. From there, organizations can begin to enforce governance, reduce unnecessary privileges, and align identity controls with the speed and scale of AI.
The broader shift is clear: AI is forcing a redefinition of identity security. It is no longer sufficient to manage users and credentials. Organizations must govern a dynamic ecosystem of human and non-human identities operating at machine speed.
In that environment, identity is not just a control point. It is the control plane. And without it, speed will continue to outpace security.
