Artificial intelligence is moving into the core of how businesses operate. What started as pilots and proofs of concept is quickly becoming part of everyday work. AI assistants, autonomous agents, intelligent applications, and automation platforms now access systems, retrieve information, interact with APIs, trigger workflows, and carry out tasks that once required direct human involvement.
That shift introduces a challenge that many organizations are still working through identity security has traditionally been designed around people, while modern enterprises now also need to govern machine-driven identities such as service accounts, application identities, APIs, bots, and AI agents.
To avoid confusion, it helps to distinguish the three identity categories discussed in this article. Human identities are users such as employees, contractors, partners, and customers. Non-human identities are machine-based identities such as service accounts, application identities, APIs, bots, and automation accounts. AI agents are a newer, more autonomous type of non-human identity that can operate across systems with less direct human involvement.
For years, identity and access management focused on employees, contractors, partners, and customers. People authenticated into systems, received access based on their responsibilities, performed their work, and eventually had their permissions updated or removed.
AI agents operate differently from traditional human users because they can act continuously, connect across systems, and execute tasks with limited human involvement.
An AI agent can work around the clock, communicate with multiple applications simultaneously, pull data from different sources, invoke APIs, and initiate business processes with little or no human involvement. Existing service accounts and application identities already perform some of these machine-driven tasks, often with elevated privileges, which is why AI agents should be governed as part of the broader non-human identity landscape.
At the same time, identity-based attacks remain one of the most persistent security challenges. Microsoft reports analyzed roughly 38 million identity risk detections on average, and its Digital Defense Report found that 97% of identity attacks involved password-spray techniques. Those numbers matter in the AI era because AI agents and other non-human identities can expand the number of access paths attackers may try to exploit.
As AI adoption accelerates, organizations need to broaden their identity security programs beyond human users. Traditional IAM controls remain important, but they must now extend to AI agents, service accounts, application identities, APIs, bots, and other non-human identities from the start.
One of the biggest obstacles for security and IAM teams is visibility.
Most organizations already manage thousands, and in some cases millions, of identities spread across cloud environments, SaaS applications, databases, legacy systems, APIs, and infrastructure. Introducing AI agents into this ecosystem adds another layer of complexity.
Over time, identities tend to accumulate access.
A service account created for a specific application may still exist years later, even though its purpose has changed. An API identity may have permissions that extend far beyond what it requires. An AI agent may inherit access from an application or user account without the proper oversight needed to govern that access safely.
The result is often the same: identities become overprivileged, dormant, ownerless, or difficult to track.
This is exactly why non-human identity governance has become such an important security discipline: it provides organizations with a structured way to discover, assign ownership of, review, monitor, and control identities that are not tied to individual human users.
Organizations need clear answers to fundamental questions:
When those questions cannot be answered confidently, identity blind spots begin to emerge.
The challenge is not only the growing number of identities. It is also the speed and scale at which machine-driven identities can operate once they have access.
A typical employee may perform dozens or hundreds of actions during a workday. An automated identity, or AI agent, can execute thousands of actions in minutes, especially when connected to multiple systems.
That speed changes the risk equation.
A compromised machine identity or misconfigured AI agent can cause significant damage long before a traditional review process detects the issue. This risk becomes more severe when attackers exploit vulnerabilities to gain initial access and then use automation or AI-assisted techniques to move faster across systems.
The faster identities can act, the more important it becomes to continuously monitor and govern them.
Periodic access reviews still play a valuable role in identity governance. Managers review permissions, certify access, and remove privileges that are no longer justified.
The problem is that modern identity environments change constantly.
