The first time it happened, nobody noticed. An automation reconciled a ledger, logged its success, and shut itself down.
The token that made it possible looked harmless. Tidy, legacy, supposedly scoped “just enough.” But a week later, refunds ghosted, dashboards blinked, and audit logs told three different versions of the truth. And that token? Not a token at all.
More like a Fabergé raptor egg sitting in a server room.
Not decoration. Incubation. Of chaos.
Just as “Jurassic Park” showed how control systems can fail spectacularly, today’s enterprises face similar challenges with autonomous AI agents. That’s where chaos theory—and identity security—come in.
AI’s evolution from generative probability to agentic chaos
Generative AI systems are still largely predictable. They’re probabilistic engines by nature—statistical, bounded by training data and prompts. They can surprise us, but they’re not truly autonomous.
They’re what I half-jokingly call AI: “The Average of the Internet.” They’re meant to return median responses by design.
AI agents, however, are another story. If we build on that “average” line above, agents are, in essence, the internet acting. Appearing non-deterministic, stateful, and feedback-driven, these systems can reason, plan, and evolve autonomously. They complete objectives, not just sentences. And once models can perform, even the smallest influences—timing differences, altered prompts, or one barely excessive permission—can send behavior down an entirely new path.
That shift from probabilistic to non-deterministic marks the boundary between machine learning and chaos theory at the enterprise level.
“Chaos Theory 101”: How tiny shifts can trigger big consequences in AI
I’m a content manager, so I’ll save us both the migraine and explain chaos theory without actual math. You’re welcome.
Chaos theory states that small changes at the start can lead to huge differences later—even in something that, on the surface, seems fairly simple. Think of a game of pool. Let’s also assume, for the sake of argument, that I’m good enough at the game—and math and physics—to calculate every angle and rate of velocity needed to predict precisely where my cue ball will strike first, bank second, and finally sink the eight ball. It should be a perfect shot.
But, even after all my painstaking calculations, I still scratch in the side pocket. Tragic.
If the math checked out, why didn’t my “perfect shot” work as planned?
According to chaos theory, it’s not randomness, even if it sounds that way. Rather, it’s environmental conditions having downstream effects. Maybe the table’s felt is scuffed, or the slate is warped—or my cue tilted 0.0001 degrees off course at the last second.
Try as I might, I can’t predict those tiny changes, which means any one of them can alter the outcome and cost me the game. If you’re looking for another example, Ian Malcolm explains it using water droplets in “Jurassic Park.” It’s also the basis for Lorenz’s butterfly effect.
The same principle that applies to the dinosaurs (and my poor pool game) applies to AI agents. A slight shift in context, data, or access can cause radically different results. If these systems fail to meet a goal, they can reason out why and evolve their errors in ways you may not consider. Every agentic decision can impact its environment, and vice versa; even seemingly linear logic becomes unmanageable after enough feedback cycles.
Just like the first few seconds after hitting a cue ball.
Identity security as the containment system for agentic AI
Under chaos theory, systems still stay within what are called attractors, which are regions of behavior that define what’s possible. Identity security can serve the same role for AI.
While it can’t prevent chaos, identity can keep it “inside the park.” In a manner of speaking.
We all know the perimeter has gone the way of the dinosaurs, so I’m not saying that identity is a paddock fence for a T. rex. Rather, I’m saying identity defines the initial conditions (who the agent is), limits the actions and feedback loops (what it can touch and do), and shapes the attractors (the bounds in which it’s allowed to operate).
Without these carefully defined boundaries, you won’t realize the full potential of innovation through AI agents. But you will get entropy.
To look at this another way, “Jurassic Park” wasn’t really about dinosaurs. It was about control systems and unintended complexity (and a healthy dose of hubris, but we’ll leave that one alone).
In the same spirit, think of today’s enterprises as their own high-tech parks—built for innovation but vulnerable to chaos when systems evolve beyond their fences. That’s the idea behind “Agentic Park.” It follows a similar pattern:
- AI agents are our digital dinosaurs, autonomous, evolving, never behaving the same way twice.
- Enterprises are the park, built on dashboards and the illusion of control.
- Identity is, again, the invisible electric fence, the keycard, the motion sensor—the things that anchor autonomy without impeding innovation.
Without identity controls, one exposed access token could be your Fabergé raptor egg: simple, elegant, and allegedly harmless.
Until it hatches.
When AI agent permissions cascade across the enterprise
A single agent with excess permissions can create local disruption. But a swarm of agents calling agents, cross-pollinating credentials, and using outputs as their own input—and so on—creates cascading failures.
AI agents may not breed the way the dinosaurs did in “Jurassic Park,” but they can still grow and swarm unchecked, probing guardrails and pushing on fences until they find the weak spots. And any shortcuts in developing parameters or implementing security controls accelerate the inevitable.
Case studies in chaos: Two agentic AI examples
If there’s any one simple means to apply chaos theory to autonomous AI systems, it’s that these agents…uh…find a way.
Here are two examples.
Case one: The first question of sensitive dependence
A finance agent is instructed to reconcile vendor data. One of its three tokens includes a deprecated endpoint that should’ve been revoked. The agent interprets that error as a permissions gap and calls a helper agent to fix it. The helper makes a correction that ripples across systems.
By the time a human takes a look, the data has “helpfully” rewritten itself.
The system behaved logically, but it still created disorder.
Case two: The swarm, chain reactions, and feedback loops
A prototype optimization agent can spawn helpers to speed up tasks, but a delay in token rotation leaves one helper credential alive longer than expected. To reduce latency, the helper shares it—and replicates.
Throughput spikes and the system scales automatically, granting “temporary” admin rights along the way. No exploit. No malicious intent. Just exponential recursion at machine speed.
A digital ecosystem behaving like a living one.
Fractals of failure—when small errors echo across the enterprise
Let’s dig a bit deeper into example two.
Chaotic systems can often produce fractal patterns, where structures repeat at every scale, creating a repeating pattern that persists across all scales. In enterprise AI, the same mis-scoping repeats from a single workflow to the entire organization: a forgotten token here, excessive permissions there, and what’s this? An overlooked integration. The pattern repeats until it spans business units and partners.
Identity is the mechanism that helps break the fractal and constrain the risk, resetting recursive calls at every layer: agent, workflow, enterprise, and supply chain.
How identity security helps fence in the unpredictable
If chaos is inevitable, containment is everything. Identity can help keep chaotic, autonomous systems operating within ordered boundaries. In other words, it makes unpredictability predictable. Ish.
To better illustrate the connection between the principles of chaos and identity controls, here’s how identity security principles map to chaos theory concepts in agentic AI:
- Initial conditions: This is our starting state, and before we can let an agent act, we have to be able to identify it and its purpose. That means every agent has to have its own identity and a clear owner.
- Feedback loops: As agents act, looping behaviors can emerge (like calling outputs as inputs, agents connecting to other agents, and so on). By scoping permissions and viewing agents as privileged machine identities, we can limit how their actions can alter their surrounding environment.
- Attractors: Governance of the full, end-to-end AI agent lifecycle—including the management of all associated certificates and secrets—is how we can define where autonomy is allowed to operate, and do so safely.
- Non-linearity: Beyond simple loops, agents can trigger complex, branching chains of actions. As these interactions multiply, so do the risks—making continuous discovery and monitoring essential to prevent escalation or misuse.
Bring order to agentic AI chaos with identity security
To return to the metaphor from the intro:
We built automations to save time, and now they’re making their own decisions. We built agents to act for us. Now they’re acting like us. Sometimes eerily so.
It’s a chaotic world, but chaos itself isn’t the threat. Rather, it’s the absence of boundaries. And Fabergé raptor eggs will always exist somewhere—elegant, plausible, lying in wait.
Identity security is how you can better ensure they can’t hatch, bite, and totally rewrite the rules. So, as your enterprise builds its own “Agentic Park,” ask yourself, what are we doing to build boundaries that secure—but don’t slow—AI innovation?
Kaitlin Harvey is a digital content manager at CyberArk.
