What Kind of Identity Should Your AI Agent Really Have?

Source:
This article is based on insights from Aembit’s original blog: https://aembit.io/blog/what-kind-of-identity-should-your-ai-agent-have/

Take a deep breath

Let’s be honest, Your AI agent is now an active participant in your systems, not just a passive tool. It’s making decisions, touching sensitive data, and moving across systems faster than we can keep up. So, the question isn’t whether it needs an identity. The real question is: what kind of identity makes sense for it?

AI Agents Are Here. Now What?

We’re entering a world where AI agents are working alongside us, responding to support tickets, writing code, even making financial recommendations. They’re fast, tireless, and autonomous.

But here’s the kicker: our identity systems weren’t built for this.

Humans have user accounts. Machines get service accounts.  AI agents? They don’t quite fit either box. And that’s where things start to break down.

The Three Identity Models We’re Seeing

Let’s break down how people are currently trying to handle this mess and why each approach has tradeoffs.

Scenario 1: When an AI Agent Acts Like a Human

Sometimes, AI agents act just like employees. Take a chatbot, for example, it accesses customer data, responds to support tickets, and talks to users just like a human would.

To make this work, we might give it a user account or let it “borrow” someone’s permissions (delegation). That’s easy to set up, but it creates problems.

• AI doesn’t have human judgment, so it might misuse broad permissions.
• What feels safe for a human becomes risky for an agent that doesn’t understand context.
• It’s harder to trace actions, was it a person or the AI that triggered a change?
• In regulated industries, like healthcare or finance, that blurred line can create real compliance risks.

Bottom line: giving AI agents human-like access may be convenient, but it comes with serious tradeoffs in visibility, control, and security.

Scenario 2: When an AI Agent Acts Like a Machine

On the other hand, we can treat AI agents like we do backend systems, give them non-human identities.

Picture an AI fraud detection system that runs 24/7. It doesn’t need a human account; it needs its own credentials and limited access.

• These agents work through APIs and automation tools not user interfaces.
• Machine credentials (like tokens, certificates, or mTLS) are ideal for this.
• This method fits well with Zero Trust, because permissions are tight and roles are clearly defined.
• It also makes auditing easier, you know exactly what the AI is doing, and when.

But here's the catch: most companies don’t actually manage machine access, they just store secrets in vaults. Without a true IAM system for machines, these identities are hard to govern. You need better tools and more discipline to get this right.

Scenario 3: When an AI Agent Is Both

Let’s be honest, most real AI agents today don’t fit into just one category. Sometimes, they act on behalf of a user (like fetching medical records for a doctor). At other times, they act on their own (like updating schedules or submitting forms). This is what we call an agentic identity, where the AI switches between human-like and machine-like roles based on the task.

• It’s flexible, but complex.
• Your IAM system needs to understand what the agent is doing at any moment, and why, then assign the right permissions accordingly.
• This might even require AI-driven decisions about identity, not just “who are you?”, but “what are you doing right now and what access do you need?”

That’s powerful but managing that kind of dynamic identity requires new tools and a more intelligent IAM setup.

What You Can Do Right Now

We don’t have full-blown IAM for AI yet, but here’s what you can start doing:

1. Classify your agents – Are they acting like humans, machines, or both?
2. Match identity to behavior – Use non-human identities for automated tasks, and delegate permissions only when needed.
3. Use scoped credentials – Only give access that’s needed, for the moment it's needed.
4. Logging – Capture not just the action, but who or what the agent was acting as when it did it.

Final Thoughts

AI agents are no longer just tools in the background, they’re active parts of your system, making real decisions and touching sensitive data. That means we can’t ignore the question of identity anymore.

The old binary model human vs machine doesn’t hold up. Some agents act like people, some act like software, and many do both. That’s why this new concept of agentic identity matters. It’s not just a technical label, it’s about giving AI the right kind of access, at the right time, with the right oversight.

Identity isn’t just about who’s behind the request anymore, it’s about what they’re doing, why they’re doing it, and whether they should be allowed to. If we want to keep AI secure and accountable, we’ve got to start treating its identity as a core part of the system, not something we tack on later.