What is a Machine Identity? Understanding AI Access Control

A machine identity is any non-human entity—software, AI agent, microservice, or automated system—that interacts with digital resources, makes decisions, or initiates actions on its own.

Where traditional machine identities were limited to API keys or service accounts, modern machine identities have evolved into far more complex actors—AI agents capable of reasoning, initiating workflows, and even acting on behalf of humans or other systems.

These machine identities aren’t just a growing trend—they’re about to outnumber human users in every system we build. While most applications have historically centered around human identities—think login forms, passwords, and user sessions—this reality is bound to change.

In this article, we dive deeper into machine identities —what they are, why they matter, and how to build access control that keeps up with them.

Some Background: The Rise of Machine Identities

When you consider how many AI agents are being embedded into software or how often external AI tools consume APIs, it becomes clear that machine identities will soon dominate our applications.

That’s a profound shift. Every product you’re building—whether AI-native or not—will inevitably have machine identities interact with it. These identities won’t just passively follow pre-set paths, either. AI agents bring dynamic, unpredictable behavior that breaks traditional assumptions about how access control works.

This raises a critical question: Are your systems ready for this? If not, it’s time to rethink how you manage identity and access—because separating humans from machines in your identity model is no longer sustainable.

We explored some of these implications in our recent piece on The Challenges of Generative AI in Identity and Access Management (IAM), where we broke down how AI is blurring the lines between users, bots, and services.

This time, we want to talk about the machine identities themselves.

What Is a “Machine Identity”?

For years, the term machine identity meant something simple—an API key, a client secret, or a service account used by a backend system to authenticate itself. These identities were static, predictable, and relatively easy to manage. They didn’t think, change behavior, or trigger unexpected actions.

That definition no longer fits.

With the rise of AI agents, machine identities have evolved far beyond static credentials. Today’s machine identities include LLMs, RAG pipelines, autonomous agents, and countless other systems capable of decision-making and autonomous action.

These aren’t just passive services waiting for input—they’re active participants, generating new workflows, accessing resources, and even spontaneously **generating new requests.

Consider a scenario where an AI agent embedded in your product needs to fetch data, process it, and call external APIs to complete a task. That agent isn’t just using its own identity—it might act on behalf of a human user, triggering a cascade of machine actions in the background.

Each step involves complex identity decisions:

• Who is really making this request?
• What permissions apply?
• Where does the human end, and the machine begin?

This is why machine identities can no longer be treated as simple backend actors. They’ve become first-class citizens in your system’s identity model, capable of performing—and demanding—the same level of access, context, and accountability as any human user.

The question is no longer if you’ll need to manage machine identities this way—but how fast you can adapt your systems to handle this growing reality.

Machine Identities Outnumbering Humans Changes Everything

It might sound dramatic—but we’re already at the tipping point where machine identities are multiplying faster than human users ever could.

Every AI agent embedded in an application, every external service calling your API, every automated system triggering actions—each represents a machine identity. And with the explosion of generative AI, the scale is no longer linear. It’s exponential.

A single human user might generate dozens of machine identity actions without even realizing it.

Their personal AI assistant triggers a query, which calls another AI service, which spins up additional agents—all cascading down a chain of machine-to-machine interactions. Multiply that across your user base, and suddenly, machine identities dominate your traffic and access control flows.

And it’s not just about your internal systems. Even if your product isn’t AI-native, chances are external AI agents are already interacting with it—scraping data, triggering APIs, or analyzing responses. These agents are users now, whether you intended it or not.

The implications for access control and security are massive:

• Static assumptions about identity volume break down.
• Traditional models that distinguish sharply between users and services create blind spots.
• Auditing who did what becomes nearly impossible if the system can’t trace actions through layers of AI agents.

Your application is already being used by more machines than humans—you just may not be tracking it yet.

That’s why the next logical step is rethinking how we approach identity management—because the current split model simply won’t scale in this new reality.

Separate Pipelines are Bound to Fail

Most applications today still run two distinct identity pipelines—one for humans, one for machines. Humans get OAuth flows, sessions, MFA, and access tokens.

Machines? They’re usually handed a static API key or a long-lived secret tucked away in a vault.

At first glance, that separation made sense. Humans are dynamic, unpredictable, and error-prone, while machines are assumed to be static, predictable, and tightly scoped.

That assumption doesn’t hold up anymore—especially with the rise of AI-driven agents acting autonomously.

AI agents don’t just perform narrow, pre-programmed tasks. They can:

• Reason based on context
• Initiate new requests mid-execution
• Chain actions that weren’t explicitly designed ahead of time
• Delegate tasks to other agents or services

Treating these agents like static service accounts creates serious risks:

• Blind spots: Machine actions happen outside your existing access control logic.
• Policy fragmentation: Developers have to maintain and reason about two different access models.
• Auditing failures: You lose the ability to track the origin of a request through layers of AI-driven activity.
• Privilege creep: Machine identities are often over-permissioned because it’s "easier" than refactoring the model.

Worse, this complexity scales poorly. As the number of AI agents grows, so does the cost of managing—and securing—two separate identity models.

We covered a version of this challenge in our deep dive into Generative AI’s impact on IAM, where we explored how these blurred lines break traditional access control. Machine identities can no longer live in a siloed pipeline. They’re too dynamic, too powerful, and too intertwined with human workflows.

The solution? A unified identity model—one that treats machine identities like first-class citizens, subject to the same rigor, rules, and accountability as humans.

Unified Identity Management

The path forward is clear: stop treating machine identities as second-class citizens in your access control model. Instead, bring them into the same identity pipeline as your human users—subject to the same policies, controls, and audits.

Unified identity management means:

• Applying the same authentication and authorization frameworks to both humans and machines
• Tracking who or what initiated every action, even when requests cascade through multiple AI agents
• Designing policies that reason about intent, relationships, and delegation, not just static credentials

There’s a lot to gain from this -

This unified approach simplifies your entire identity model, eliminating the need to juggle separate systems and reducing complexity for both developers and security teams.

It strengthens accountability by allowing you to trace even the most complex chains of machine-driven actions back to their original source—understanding which AI acted on behalf of which human.

And most importantly, it scales. As machine identities inevitably grow and evolve, your access model remains resilient, able to handle the volume and complexity without breaking or creating new blind spots.

This is exactly the kind of shift we discussed in our guide to AI Security Posture Management (AISPM), where we explored how modern systems must handle AI agents, memory, external tools, and dynamic interactions—all within a unified framework.

Unifying your identity model doesn’t mean machines and humans lose their differences. It means recognizing that both deserve equally robust access control, tailored to their behaviors, risks, and relationships. AI agents might act differently than humans—but the need to verify their actions, track their permissions, and audit their behavior is just as real, if not more so.

Because in the world we’re rapidly entering, machine identities won’t just participate in your systems—they’ll dominate them. The question is whether your access model is ready for that shift.

Human Intent as the Source of Machine Actions

At the heart of this challenge is a simple fact: machine actions almost always originate from human intent. Whether it’s an AI assistant fetching data, an automated agent triggering a workflow, or a third-party service interacting with your API—somewhere, a human set that action in motion.

The problem is that traditional access control models rarely capture that nuance. Once a machine identity takes over, the connection to the human gets lost in translation. Requests appear isolated, making it nearly impossible to trace a decision back to the person who authorized it—or even know if there was human authorization in the first place.

This is where the concept of "on behalf of" relationships becomes critical. Systems need to recognize not just who is performing an action, but why and for whom. Every AI agent operating inside your app—or consuming your services externally—should carry that context forward. Only then can you enforce policies that properly reflect the human’s intent, not just the machine’s behavior.

We explored this deeply in our recent article on managing AI permissions and access control with Retrieval-Augmented Generation (RAG) and ReBAC. AI agents acting autonomously must inherit—and be limited by—the access rights of the humans they represent. Anything less opens the door to unintended data exposure, overreach, or worse, AI agents making decisions no human ever authorized.

Maintaining this chain of accountability ensures that machine identities don’t just act—they act within the scope of human intent. As AI agents become more capable and complex, this connection keeps your system secure, auditable, and aligned with your users’ expectations.

AI Capabilities Force Rethinking Access Models

What makes AI-driven machine identities so challenging isn’t just their volume—it’s their behavior. Unlike traditional services that follow predictable, predefined tasks, AI agents are dynamic by design. They can generate new actions mid-process, chain multiple requests, delegate tasks to other agents, and even identify additional resources they "need" to complete a goal—all without explicit, step-by-step instructions from a developer.

This level of autonomy breaks traditional role-based access control (RBAC) models. RBAC was built for static environments where permissions are tied to well-defined roles and rarely change in real-time. But AI agents don’t fit neatly into predefined roles—their actions depend on context, data, and the evolving nature of the task at hand.

To manage this complexity, systems need to move beyond static roles and embrace Relationship-Based Access Control (ReBAC). Unlike RBAC, ReBAC evaluates access based on the relationships between entities—the AI agent, the data it’s trying to access, the human it represents, and even the context of the request. It’s not just about what an identity is allowed to do; it’s about why the identity is acting, on whose behalf, and under what conditions.

This shift is critical as AI agents increasingly operate autonomously within systems. Without relationship and context-aware policies, AI agents risk overstepping, accessing resources they shouldn’t, or unintentionally triggering cascading actions that are difficult—if not impossible—to audit.

In our deep dive into dynamic AI access control, we explored how modern systems must adapt to these AI-driven dynamics by implementing real-time, event-driven policy checks. ReBAC is one of the most effective ways to capture the nuanced relationships AI introduces and ensure access is granted only when it aligns with both policy and human intent.

Practical Implementation Patterns

Translating these concepts into practice means rethinking how your system handles identity checks, delegation, and auditing—especially as AI agents take on increasingly complex roles. Fortunately, there are already tools and patterns designed to help.

One powerful pattern is the check_agent() approach, which explicitly captures delegation and "on behalf of" relationships in your access control logic. Rather than just checking if an agent has permission, this method evaluates who the agent is acting for and what context applies.

For example, instead of a traditional Permit.io access control check like:

permit.check(identity, action, resource)

You shift to:

permit.check(
  {
    key: agent_identity,
    attributes: {"on_behalf": [user_identity]}
  },
  action,
  resource
)

This ensures that access decisions account for both the AI agent’s permissions and the human it represents—enforcing delegation boundaries and preventing unauthorized access chains.

Permit.io supports this pattern natively, enabling applications to enforce fine-grained, relationship-aware policies. Similarly, tools like OPAL (Open Policy Administration Layer) help synchronize policies and fetch dynamic data—like current relationships or risk scores—so that every check reflects real-time context.

For scenarios involving AI agents operating with varying confidence levels or risk profiles, you can also incorporate identity ranking systems like ArcJet. Rather than treating all machine identities equally, ArcJet scores them based on behavioral signals—allowing your system to apply stricter policies to low-confidence actors and more flexible ones to verified agents.

These practical patterns don’t just improve security—they make your system more auditable. Every AI action carries its origin, context, and reasoning, allowing you to trace the full chain of decisions if something goes wrong.

As we explored in our article on managing AI permissions and RAG pipelines, these patterns become especially powerful when applied to complex AI workflows where agents interact with external tools, memory stores, and sensitive resources.

Preparing for the Machine Identity Majority

Machine identities aren’t coming—they’re already here. And soon, they’ll vastly outnumber human users in every system you build. AI agents, automated services, and autonomous workflows are no longer background processes—they’re active participants in your application, making decisions, triggering actions, and consuming resources.

The old way of handling identity—splitting humans and machines into separate, static pipelines—simply won’t scale in this new reality. The future of identity and access control depends on unifying your model, treating machine identities as first-class citizens, and ensuring every action—human or machine—can be traced, authorized, and audited.

The good news? The tools and frameworks to do this already exist. Whether it’s leveraging ReBAC, implementing on-behalf-of delegation patterns, or adopting real-time dynamic access control—you can start building systems today that are ready for the machine identity majority.

If you’re interested in diving deeper into this shift, check out our full series on AI identity challenges:

• The Challenges of Generative AI in Identity and Access Management (IAM)
• Where Can They Go? Managing AI Permissions
• The When – Dynamic AI Access Control for a Changing Timeline

Because the question is no longer if machine identities will dominate your systems—it's whether your access model is ready for them when they do.

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