AI identity is not yet a fully formed concept.
There is a concept of human (workforce and client) identity, and non-human (applications, workloads, scripts) identity, but as you explore, AI requires a bit of both. And that could mean something completely new.
Developers may not be thinking about what an AI identity is or how it should be managed. Instead, they think about how to get agents to do what they want.
And there’s nothing wrong with this. As an emerging technology, the first hurdle for Agent AI is to achieve new and powerful results. Everything else follows.
We hope that our industry has learned well enough to build security from the start when agents enter into mainstream enterprise use. (Hey, I can dream, can I not?)
There are many factors to consider in Agent AI Security, but we focus on talking to experts about the complex challenges of AI agents’ identity management.
The core of this challenge is the important questions. What identity does an AI agent need to own? Should your AI agent adopt a human-like identity, strictly non-human identity, or something else?
Ask the three developers and get four opinions. In such a rapidly evolving space, these answers change just as quickly. But let’s use this opportunity to frame the discussion.
Why Identity is important to Agent AI
We’ve all heard the saying “Identity is a new boundary,” but the identity of AI agents introduces a new class of challenges.
The identity itself offers many benefits. Identity Control Access, Identity is a form of security, and Identity forms the basis of auditing. Therefore, the idea of having an AI identity is not overstated. Traditional human identity systems are designed around predictable behavior, stable access patterns, and long-lived entities. In contrast, AI agents are dynamic, transient, and autonomous.
In the latter case, Agent AI sounds a lot like a non-human identity (NHI). Read the link and compare yourself. In this context, agents appear to be spin-up, work, and shut down workloads.
So did the case close? AI is not human, right? It’s not that fast.
One of the major differences between AI agents and NHIS is their deterministic nature. NHIS, like applications and scripts, has a static set of features, workflows that require provisioning, even if the workload is very short-lived.
Agents may spin up on demand, invoke APIs across domains, and generate actions based on their own inferences rather than directly on human input. Therefore, managing the identity of AI agents must address not only fundamentals such as authentication, authorization, and auditability, but also deeper concerns about autonomy, delegation, contextual reasoning, and lifecycle boundaries.
AI agents are designed to take the actions necessary to achieve their goals – Often there is no fixed sequence or predefined access patterns. As a result, those behaviors may vary from one activity to another, resulting in non-deterministic outcomes, non-deterministic actions and therefore non-deterministic needs, from the next attempt to the next.
Essentially, AI agents push identity boundaries beyond static provisioning. They simply need an identity system that is not reactive But it is predictive and adaptive – You can understand and enforce identity attitudes in an environment where actors are constantly changing roles, contexts and risk profiles.
AI identity is strictly the “it depends” type of answer today, but hopefully, as space matures, you can answer it more specifically. In the meantime, let’s look at three different AI agents and see how their requirements drive identity requirements.
