When the Attacker is an AI: Turning the Tables with Preemptive Security

The cloud has always been a battleground, but the terrain is shifting under our feet. As organizations across the globe rush to integrate Generative AI—deploying Foundation Models for inference, training proprietary models in managed ML environments, and connecting agents via API gateways—the definition of “critical infrastructure” has changed.

The new crown jewels aren’t just customer databases anymore; they are your model weights, your vector stores, and your training pipelines.

And naturally, where the value shifts, the attackers follow.

But here is the concerning reality: we are no longer just facing human hackers writing scripts. We are facing sophisticated adversaries utilizing AI Agents to scan, adapt, and exploit cloud environments at machine speed.

The era of AI-driven cyberattacks is no longer hypothetical; it is here. A recent report by Anthropic, Disrupting AI Espionage, detailed the first documented case of a large-scale cyberattack executed without substantial human intervention. In this campaign, assessed to be the work of a state-sponsored group, adversaries utilized AI agents to perform 80-90% of the operation.

The attackers “jailbroke” the model to bypass safety guardrails, instructing it to inspect target systems, identify high-value data lakes, and even write its own exploit code to harvest credentials.

What makes this terrifying is the velocity. The AI agent was capable of making thousands of requests—often multiple per second—a speed that is simply impossible for human hackers to match. This fundamental shift dramatically lowers the barrier to entry for sophisticated espionage, allowing attackers to scale their reconnaissance and exfiltration efforts autonomously. Against an adversary that can test thousands of permission combinations across your cloud identity provider in seconds, reactive defense is already too late.

To protect these high-value AI workloads, we need to stop playing catch-up. We need Preemptive Security.

The most effective way to counter an AI-driven adversary is to turn their own speed and automated curiosity against them. This is where honeytokens come into play.

A honeytoken is a digital asset—a credential, a configuration file, an API key, or a Cloud Identity—that has no legitimate business function. It serves as a high-fidelity tripwire. When deployed strategically across your cloud AI workloads, these honeytokens create a minefield for unauthorized agents.

Our approach utilizes a specialized recommendation engine to map your specific AI footprint, regardless of whether you run on AWS, Azure, GCP, or Oracle. Whether you are heavy on inference, deep in training, or rolling out agentic workflows, we deploy honeytokens that look indistinguishable from your real assets.

1. The Honeytokens (Deceptive Identities)

We deploy “Honey Identities” that mimic high-privilege accounts. To an adversarial AI agent scanning the environment, these look like the keys to the kingdom. They might appear to be a “Model Administrator” with invocation rights, a “Data Scientist” with access to training clusters, or a “Vector DB Auditor” with access to proprietary embeddings.

2. The Open Door

These identities are often configured with access policies or trust relationships that appear slightly permissive—just enough to entice an automated agent to prioritize them as a target.

3. The Empty Room

Once the adversary attempts to authenticate as the user, assume the role, or use the service principal, they find it has no actual permissions. They are trapped in a digital dead end.

4. The Snap (Zero-False-Positive Alerting)

The moment that identity is used, an alert is triggered. Because no legitimate employee, pipeline, or service should ever touch these specific assets, this signal is pure. We detect the adversary during their reconnaissance phase, long before they can damage your actual models or steal data.