An employee with persistent, unsupervised admin access across critical systems—no audit trail, no clear owner, no regular access reviews—would raise immediate alarm in most organizations. Yet non-human identities (NHIs) and AI agents are routinely granted that same level of broadly privileged access. As AI adoption accelerates, this gap is becoming impossible to ignore.

NHIs today encompass far more than traditional service accounts and API keys. They include AI agents that make autonomous decisions, automated workflows with cross-system access, and shadow AI tools deployed by business users without IT oversight. These machine identities operate at machine speed, making them fundamentally different from human users.

Why the NHI double standard exists

Three interlocking factors drive this double standard, each reinforcing the others to create a cycle of compromised identity governance.

Priority of speed over governance

Business pressure to deploy AI initiatives quickly means identity controls are often relaxed or skipped entirely. According to a recent survey of IT decision-makers, 90% of organizations place pressure on security teams to loosen access controls to support AI-driven automation. When tension arises between security requirements and business speed, fewer than one in three organizations enforce security requirements consistently. This trade-off creates a dangerous precedent where short-term productivity gains override long-term security posture.

Poor monitoring of shadow AI

Unsanctioned AI agents operate outside any governance framework. A significant 53% of surveyed organizations regularly encounter unauthorized AI tools and agents accessing company systems. These deployments bypass traditional provisioning processes, creating unmonitored access points that security teams struggle to detect. Shadow AI often arises from business users trying to improve efficiency without waiting for IT approval, but the result is a sprawling, invisible attack surface.

Unchecked NHI activity

Traditional identity management systems rely on predictable, human-centric workflows. Legacy IAM tools lack the velocity and dynamic capabilities needed to govern autonomous agents that make independent decisions and request elevated privileges without warning. Human users follow patterns—logging in at certain times, from certain locations, performing routine tasks. AI agents do not. They scale horizontally, execute tasks in parallel, and can request privileges from hundreds of endpoints simultaneously. This behavior breaks the assumptions built into most identity solutions.

The operational reality makes this challenge even more complex. Survey data shows that 74% of organizations say standing access for NHIs and AI agents is necessary to meet uptime expectations. Meanwhile, 59% report they lack viable alternatives to persistent access for these accounts. This creates a situation where security teams knowingly accept risk under operational pressure, a practice that would be unacceptable for human users.

Closing the AI identity risk gap

Organizations must confront the AI security confidence paradox. Expressing high confidence in AI readiness despite knowing there are fundamental AI-related identity governance gaps occurs because information is incomplete. Security teams cannot protect against what they cannot see. Consider this: 82% of organizations report confidence in their ability to discover NHIs with access to production systems, but fewer than one in three actually validate NHI and AI agent activity in real time. The vast majority of IT decision-makers admit to at least some identity visibility gap, with NHIs representing the largest blind spot.

Step 1: Visibility

Before implementing new access controls or policies, organizations must establish a clear inventory of which NHIs exist—including shadow AI use—what they have access to, and whether any of that access is standing or persistent. Without foundational visibility, any governance efforts become guesswork rather than risk-based decision-making. Automated discovery tools can map machine identities across cloud and hybrid environments in real time, flagging orphaned accounts, unused credentials, and unauthorized agents.

Step 2: Zero standing privilege

Just-in-time and ephemeral access represent the goal, even if they are not immediately achievable for most organizations. The survey shows organizations are more than twice as likely to use long-lived credentials (34%) compared to modern just-in-time authorization (16%). As one industry expert noted, “I’ll count it as a win if we just have an inventory of all the identities that have standing access.” The path to zero standing privilege requires incremental improvements: start by eliminating persistent access for low-risk NHIs, then expand to more critical systems over time.

More practical governance tips include: watching for NHIs requesting elevated privileges unexpectedly because it often signals either compromised accounts or poorly configured automation; flagging accounts with no clear owner or business justification for immediate review; and treating NHI access reviews with the same rigor applied to human access reviews, including regular certification and deprovisioning of unused accounts.

Building secure AI without slowing innovation

Halting AI adoption is not an option. The reality-based goal is closing the visibility gap that allows risky access patterns to persist undetected. Organizations need automated discovery tools that can map machine identities across cloud and hybrid environments in real time. Governance frameworks must operate at speed without the friction that drives teams to bypass strict oversight. This requires upgrading identity infrastructure to handle the velocity and unpredictability of agentic AI. Security teams can satisfy business demands for speed without abandoning identity governance entirely.

The risk of non-human identities is not theoretical. In 2025, several high-profile breaches traced back to compromised service accounts and AI agent tokens that had no owner, no audit trail, and no expiration. Attackers increasingly target NHIs because they offer a quieter path to lateral movement. A single AI agent with read access to a sensitive database can be hijacked to exfiltrate data without triggering human-centric detections. As AI agents become more autonomous—reasoning, planning, executing multi-step workflows—the potential for damage grows exponentially.

Organizations that ignore the NHI governance gap will find themselves facing audit failures, regulatory penalties, and preventable incidents. Regulators are starting to take notice: the European Union’s AI Act and various data protection authorities are examining how machine identities are managed. Boards are asking tough questions about AI risk. Answers must include a concrete plan for NHI lifecycle management.

The path forward requires a shift in mindset. Identity security can no longer be human-centric. It must become identity-agnostic, treating every entity—human, machine, agent—with the same level of scrutiny and control. This means applying the principle of least privilege to every API key, every service account, every AI agent. It means building a catalog of every machine identity and continuously monitoring their behavior for anomalies. It means investing in tools that can enforce policies at machine speed. Only then will organizations achieve the AI adoption they desire without compromising their security.

Source: Help Net Security News