Machine Identity Security: Managing Risk, Delegation, and Cascading Trust

The term “Machine identities” changed drastically in the past couple of years, with AI becoming a core part of basically every application in the market.

The machine identities we know today aren’t scripts hidden in the backend—they’ve evolved into autonomous agents, **making decisions, initiating actions, and triggering workflows—**sometimes on behalf of humans and sometimes without direct human involvement whatsoever.

The thing is, most of our existing security models aren’t designed for this reality. They’re used to treating machine identities as static service accounts or API keys—tools that perform narrow, predefined tasks.

But that’s far from the reality of today’s applications, resulting in a surge of complexity in managing risk, delegation, and trust.

The challenge we face today is no longer about authenticating machine identities or managing credentials—it’s about securing what those identities do, how they interact, and how trust flows through your system.

In this article, we’ll explore the evolving risks around machine identities, break down why delegation and cascading trust are the next major challenges in AI security and share practical strategies for building systems that can model, monitor, and contain these risks at scale.

To understand why this shift matters, let’s start by looking at how general system security differs from machine identity security.

Machine Identity Security vs. General System Security

When developers think about securing their systems, they often focus on the usual suspects—data protection, API security, and infrastructure hardening. These are all critical but are just one part of the picture.

Machine identities themselves have become a security surface—one with unique risks that traditional system-level security doesn’t fully cover.

The mistake is assuming that machine identities are just background processes—tools designed to follow strict, predefined paths. But modern AI agents and machine-driven services don’t behave like that. They operate independently, make real-time decisions, and interact dynamically with other services, agents, or APIs.

That shift turns machine identity into something much more active—and potentially dangerous if not properly secured.

It’s at this point where machine identity security diverges from general system security.

The challenge that machine identity security presents us with is the recognition of machine identities themselves as actors—ones that can carry out actions, represent other users or systems, and propagate trust or risk as they move through your architecture.

This requires more than just credential rotation or static access policies. It demands identity-level reasoning—understanding what a machine identity is authorized to do, for whom, and under what conditions. It also means being able to track and audit those actions with the same level of scrutiny you’d expect from a human user.

Delegation — The Hidden Risk in AI-Driven Systems

One of the biggest challenges with machine identities—especially AI agents—is that they rarely act in isolation. More often than not, they’re acting on behalf of someone or something else.

Maybe it’s an AI assistant fetching data for a user. Maybe it’s a service triggering a downstream action in another system. Or maybe it’s an agent calling yet another agent, cascading through multiple layers of delegation.

In reality, most access control models aren’t designed to handle this kind of complexity.

They check who is making a request but rarely ask why that request is happening or on whose behalf. In AI-driven systems, these are a must to get a full picture of access control.

Without clear delegation boundaries, it becomes nearly impossible to know:

Who initiated the action
Which agent or service is carrying it out
Whether that action is actually authorized in the first place

The result is a growing risk of unauthorized access, where machine identities end up performing tasks or getting access to data far beyond what the original human or system intended—simply because the delegation chain wasn’t properly enforced or auditable.

Modeling Machine Trust, Risk, and Accountability

Securing machine identities isn’t just about handing out permissions—it’s about understanding how those identities relate to each other and why they’re allowed to act in the first place. This makes this a perfect fit for Relationship-Based Access Control (ReBAC).

Unlike traditional models like Role-Based Access Control (RBAC), where access is tied to static roles, ReBAC models your system as a graph—one where users, machine identities, services, and resources are all connected by relationships.

Instead of asking, "Does this identity have the right role?", ReBAC allows you to ask:

"What is the relationship between this machine identity and the resource it’s trying to access?"
"Is this agent acting on behalf of someone who has the necessary rights?"
"Does this action align with the permissions inherited through that relationship?"

This matters because, in AI-driven systems, relationships constantly change—identities delegate, agents chain actions, and new connections form dynamically. A static role isn’t enough to capture that complexity.

Policy as a Graph

With ReBAC, your policy becomes a living graph. Nodes represent users, agents, services, or resources, and edges represent the relationships and context that connect them—like "owns", "acts on behalf of", "is a sub-agent of".

This structure allows for role derivation based on position in the graph rather than relying on hardcoded roles:

If an AI agent is connected to a user via “personal assistant”, it may inherit certain permissions from that user—but only within that defined context.
If a third-party service is "contracted by" your system, it may gain access only to specific datasets or actions—and nothing beyond that.

It’s a flexible, scalable model—one that reflects how complex systems actually behave.

Modeling ReBAC as a graph in Permit.io

The Google Zanzibar Influence

Google’s Zanzibar system is one of the best-known examples of ReBAC at scale. It powers fine-grained access control for products like Google Drive, Calendar, and YouTube—where permissions are derived based on relationships between users, groups, and resources.

Zanzibar doesn’t just check roles. It traverses a graph of relationships to answer complex questions like:

“Can this person view this document because they’re part of a group that was shared access via another group?”

In the context of machine identity security, the same model applies. Only now, it’s not just humans in the graph—it’s machine identities, AI agents, and third-party services.

Why This Matters for Machine Identity Security

As machine identities grow more complex and autonomous, relationship context becomes the only reliable way to enforce boundaries and accountability. They let you trace the "why" behind every action—not just the "who", prevent agents from overstepping their authority because they were assigned a broad role, allow dynamic delegation, where temporary permissions flow based on relationships (not permanent roles). On top of this, they make auditing possible: you can always ask,

“How did this AI get access to this resource—and was it authorized based on its relationships?”

Without ReBAC, AI systems risk becoming opaque black boxes, where actions happen, but no one can explain why. ReBAC ensures every action is rooted in a defined, traceable relationship—making machine identity security enforceable, scalable, and auditable.

Best Practices for Securing Machine Identities

Securing machine identities is about building systems that understand what these identities represent, what they’re allowed to do, and how trust flows through them—especially as they interact, delegate, and act on behalf of others.

Here are a few core best practices that help bring machine identity security into focus:

Build Security Into the Identity Itself

Machine identities—especially AI agents—should carry their security context with them. That means:

Self-describing permissions that define what they can and cannot do
Clear limits on who or what they’re authorized to act for
Embedded identity verification every time they initiate an action, not just at session start

Relying solely on perimeter checks or external validations leaves too many gaps once machine identities start making independent decisions downstream.

Model Relationships and Delegation Explicitly

Leverage ReBAC to define how machine identities are connected to the resources or actions they’re trying to access.

This allows you to differentiate between a trusted first-party agent and a third-party service acting on behalf of a user, control how far delegated permissions propagate, and prevent privilege creep by ensuring each action is evaluated in context, not based on static roles.

Introduce Risk Scoring and Trust Boundaries

Not every machine identity should be trusted equally. Systems should assign risk scores or trust levels to agents based on:

Who created or owns them
Their behavior over time
The sensitivity of the tasks they’re performing

For critical actions, require elevated trust levels or insert human-in-the-loop approvals to prevent automated agents from making unchecked decisions.

Implement Time-to-Live (TTL) on Trust

One of the biggest risks in AI-driven systems is unbounded trust propagation—where agents keep delegating actions indefinitely.

To prevent that:

Set TTL limits on delegated permissions
Require re-authorization as actions pass through multiple layers of agents or services
Ensure trust doesn’t cascade further than it was ever meant to

This prevents scenarios where an attacker or misbehaving agent exploits downstream trust to trigger unintended actions.

Make Every Action Auditable

Finally, design systems where every machine action is traceable:

Log who initiated the action
Record on whose behalf it was performed
Capture the full delegation chain leading to that decision

Without this, machine-driven systems become impossible to audit—making it hard to spot abuse, track incidents, or prove compliance.

The Bottom Line

Machine identity security doesn’t stop at authentication. It’s about controlling what these identities do once they’re active, how far their permissions extend, and how trust moves through your system.

Building systems that handle this well isn’t just good practice—it’s critical for keeping AI-driven systems safe, predictable, and accountable.

Machine Identity Security Keep AI Systems Accountable

Machine identities are no longer just background services quietly executing tasks—they’re active participants in our systems, making decisions, triggering actions, and interacting with other agents, sometimes completely autonomously.

That shift introduces a whole new set of risks—delegation chains, trust propagation, and the growing challenge of tracking who’s really responsible for what.

The only way to manage that complexity is to rethink how we secure machine identities—not just by controlling their credentials, but by modeling their relationships, limiting their delegated authority, and building systems that treat their actions as first-class security concerns.

Whether it’s leveraging ReBAC models, adding risk scoring, or enforcing TTL on trust, these are no longer theoretical ideas—they’re becoming necessary building blocks for securing AI-driven systems.

At the end of the day, machine identity security is what keeps your system’s decision-making process safe, accountable, and under control—no matter how complex the architecture becomes.