What Kind of Identity Should Your AI Agent Have? – Source: securityboulevard.com

AI identity is not yet a fully formed concept.

We have a concept of identity for humans (workforce and customers), and we have  identity for non-humans (applications, workloads, scripts), but as we will explore, AI requires a little bit of both. And that may mean something new entirely.

It’s likely your developers are not thinking about what an AI identity is, nor how it should be managed. They are thinking, instead, about how to make an agent do what they want it to do.

And there’s nothing wrong with this: As an emerging technology, agentic AI’s first hurdle is to achieve new, powerful outcomes. Everything else will follow.

We have hopefully learned enough in our industry such that, as agents come into mainstream enterprise use, we will build security in from the start. (Hey, I can dream, can’t I?)

While there are many factors to consider with agentic AI security, I’ve been focusing my time talking to experts about the  complex challenge of identity management for AI agents. 

Central to this challenge is a crucial question: What kind of identity should your AI agent possess? Should your AI agent adopt a human-like identity, a strictly non-human identity, or something else?

Ask three developers and you’ll get four opinions. In such a quickly evolving space, these answers will change just as rapidly. But let’s use this opportunity to frame the debate.

Why Identity Matters for Agentic AI

We’ve all heard the adage “Identity is the new perimeter,” but identity for AI agents introduces a new class of challenges.

Identity itself provides many benefits. Identity controls access, identity is a form of security, identity forms a basis for auditing. Thus, the idea of having an identity for AI isn’t far-fetched. Traditional human identity systems are designed around predictable behaviors, stable access patterns, and long-lived entities. In contrast, AI agents are dynamic, ephemeral, and autonomous. 

In that latter case, agentic AI sounds a whole lot like a non-human identity (NHI). Read the link to compare it for yourself, but in this context, agents appear like workloads that spin up, do their job, and shut down. 

So, case closed? AI is non-human, right? Not so fast.

One of the big differences between AI agents and NHIs is their deterministic nature. NHIs, like applications and scripts, have a static set of capabilities, workflows, and hence permissions they need provisioned, even if the workloads are highly ephemeral.

Agents may spin up on demand, call APIs across domains, and generate actions based on their own reasoning rather than direct human input. As such, managing identity for AI agents must accommodate not only the basics – authentication, authorization, and auditability – but also deeper concerns around autonomy, delegation, contextual reasoning, and lifecycle boundaries.

AI agents are designed to take whatever actions are necessary to accomplish a goal – often with no fixed sequence or predefined access pattern. As a result, their actions may vary from one activity to the next, and even from one attempt to the next, resulting in non-deterministic outcomes, non-deterministic actions, and hence non-deterministic needs.

In essence, AI agents push the boundaries of identity beyond static provisioning. They require identity systems that are not just reactive but also predictive and adaptive – capable of understanding and enforcing identity posture in environments where the actor is constantly shifting roles, context, and risk profile.

Identity for AI is strictly an “it depends” type of answer today, but hopefully as the space matures we can answer it more concretely. In the meantime, let’s take a look at three different AI agents and see how their requirements drive an identity requirement.