The Only Way to Secure AI in the Cloud

AI is not just another workload category.

It is the first category of workloads that decides what to do at runtime.

And that changes everything about how security must work in the cloud.

For years, cloud security evolved around deterministic systems. You deploy code. That code follows defined logic paths. If something unexpected happens, such as a new process, an unusual outbound connection, or privilege escalation, you investigate and respond.

AI agents break that model since they:

• Generate code
• Invoke tools dynamically
• Retrieve and chain data
• Act across identities
• Change behavior based on prompts

From the infrastructure layer, it looks like an anomaly, but from the agent’s perspective, it’s just doing its job.

That gap between behavior and intent is the new security frontier.

AI Workloads Don’t just Run, they Behave

Traditional workloads are bound by what developers wrote. AI workloads are bounded by what they are allowed to access.

An AI agent can:

• Generate new execution paths that no one reviewed.
• Call APIs that were technically permitted but never expected.
• Modify behavior over time without any configuration change.

Nothing in the container image changes.
Nothing in Kubernetes RBAC changes.
Nothing in IAM policies changes.

In AI systems, behavior is the attack surface.

The compromise unfolds entirely in runtime, and if your security model only evaluates configuration, you will miss it.

The ARMO Behavioral AI Security Platform

Deep Runtime Observability

The ARMO platform begins by seeing exactly what AI workloads are doing — not just what was declared in manifests or deployment templates. Traditional security tools are blind to the reality that matters: the actual models, agents, and runtime components in play across your cloud environments. ARMO automatically detects AI agents, inference servers, frameworks like LangChain and AutoGPT, and related components as they live in your clusters. It then builds a runtime-derived AI Bill of Materials (AI-BOM),  a continuously updated view of models, libraries, and execution paths. This gives security teams a live understanding of what their AI footprint truly looks like in production.

AI-Native Threat Detection

Generic container or workload alerts are inadequate when the danger originates from prompt manipulation and tool misuse. ARMO’s detection layer speaks the language of AI. Instead of alerting on surface-level anomalies like “unexpected process started,” ARMO contextualizes events to show whether an instance of behavior is a benign inference or a sign of an Agent Escape, prompt-driven exploitation, or malicious tool interaction. This means security teams can confidently distinguish true threats from noise and focus their response where it matters most. 

Progressive Behavioral Sandboxing

Static policy enforcement fails in the face of dynamic, prompt-driven systems. ARMO’s progressive sandboxing approach closes that gap. Teams observe real workload behavior, allow the platform to learn baselines, and then derive least-privilege policies based on that empirical evidence. Enforcement happens in a cloud-native way through Kubernetes-native and cloud-native sandboxing that limit network, API, file, and process access without requiring code changes or disruptive rewrites. This means compromised logic stays contained, and legitimate AI behavior continues uninterrupted.

Intelligent Security Posture (AI-SPM)

Real risk isn’t just about what an AI workload might be able to do, it’s about what it actually can do in your environment. ARMO’s AI Security Posture Management continuously evaluates risk by combining real-time behavioral visibility with permissions, identities, and known vulnerabilities. This allows teams to identify excessive privileges, weak isolation boundaries, and exploitable AI-specific runtimes or toolkits before they become incident vectors.

Rich Execution Mapping

Understanding AI security requires more than isolated signals, it requires mapping an entire chain of behavior: Agent → Tool → API → Data → Identity. ARMO visualizes these execution flows so teams can trace an incident from the initial prompt through to data access or action execution. This contextual mapping transforms raw telemetry into actionable, explainable findings that accelerate both detection and investigation. 

Why This Matters Now

As AI workloads and Agents are reaching the production environment and running within cloud and Kubernetes as applications, the traditional security mode that relies only on visibility and governance is no longer sufficient. AI workloads are not misconfigurations. They are autonomous behaviors. Security must be built around behavior, not just inventory or governance.

ARMO’s Behavioral AI Security Platform goes beyond what most cloud security products currently offer by:

• Detecting AI threats in context
• Profiling behavior in real-time, continuously
• Mapping complete execution flows from agent to data
• Enforcing least privilege based on observed patterns
• Sandboxing behavior without breaking production

This is what it means to secure AI in the cloud, not as another checkbox in a posture dashboard, but as a first-class runtime concern that aligns with how AI agents actually operate. In a world where cloud applications don’t just run, they behave —> Security must evolve to match.