An employee with persistent, unsupervised admin access across critical systems, with no audit trail, no clear owner, and no regular access reviews, would raise immediate concern in most organizations. Yet non-human identities and AI agents are often granted that same kind of persistent, broadly privileged access. As AI adoption grows, this gap is becoming harder to ignore.

Non-human identities today encompass far more than traditional service accounts and API keys. They also include AI agents that make autonomous decisions, automated workflows with cross-system access, and shadow AI tools deployed by business users. These entities operate at machine speed, often requesting elevated privileges without warning, and are not bound by human-centric authentication cycles.

Security teams often believe they are prepared for AI adoption at scale. A recent survey of IT decision-makers found that 87% of organizations say their identity security posture is ready. However, the same research reveals that NHIs operate with speed and behavior patterns that legacy controls were not designed to handle. In fact, 46% of respondents admitted that their AI identity governance is deficient. This dissonance represents a risky double standard in enterprise security.

Why the NHI double standard exists

Three fundamental 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 often means identity controls get relaxed or skipped entirely. The survey found that 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 creates a culture where short-term innovation wins over long-term security hygiene, leaving NHIs with unchecked privileges that can be exploited.

Poor monitoring of shadow AI

Unsanctioned agents operate outside any governance framework entirely. 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 tools—such as personal assistant bots, automated report generators, and code-completion agents—are often set up by employees without IT knowledge, creating blind spots that attackers can leverage.

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. The operational reality makes this challenge even more complex. According to the survey, 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.

The implications are profound. A compromised NHI can act as a pivot point for lateral movement across cloud and on-premises environments. Since NHIs often have access to multiple systems—sometimes more than human users—a single breach can lead to widespread data exfiltration or system disruption. Moreover, the lack of audit trails for autonomous agents makes incident response difficult, as there is no clear record of what actions were taken by which agent.

What does closing the AI identity risk gap require?

Organizations must confront the AI security confidence paradox. Expressing high confidence in AI readiness despite knowing there are fundamental AI-related identity governance gaps happens 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 sort of 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 unknown or unowned accounts.

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%). Transitioning to zero standing privilege requires a mindset shift: instead of always-on access, NHIs should receive temporary privileges only when needed, and those privileges should automatically expire. This reduces the attack surface and limits the blast radius of any compromise.

More practical governance tips include:

• Watch for NHIs requesting elevated privileges unexpectedly because it often signals either compromised accounts or poorly configured automation.
• Flag accounts with no clear owner or business justification for immediate review.
• Treat NHI access reviews with the same rigor you apply to human access reviews, including regular certification and deprovisioning of unused accounts.

Additionally, organizations should implement behavioral analytics for NHIs. Since machine identities have predictable patterns—such as connecting from specific IP ranges at certain times—deviations can be detected and flagged. For example, an AI agent that suddenly begins accessing databases outside its normal scope should trigger an alert.

Another important step is to integrate NHIs into identity governance and administration (IGA) platforms. Many organizations manage human identities well but ignore machine identities. By treating NHIs as first-class citizens in IGA, policies around provisioning, deprovisioning, and certification can be uniformly applied.

Training and culture also matter. Security teams should educate developers and business users about the risks of shadow AI. Encouraging the use of sanctioned tools and providing a secure sandbox for experimentation can reduce the temptation to bypass controls.

Finally, consider the role of AI itself in managing NHIs. Machine learning models can analyze patterns of NHI behavior and automatically recommend access adjustments. This can help scale governance without adding manual overhead.

The hidden risk of non-human identities in AI adoption is not a theoretical problem—it is a present and growing challenge. As organizations race to deploy AI, the security of these machine identities must be a priority. By focusing on visibility, zero standing privilege, and continuous monitoring, security teams can close the gap without slowing innovation.

Source: Help Net Security News