Wallarm AI Hypervisor: Runtime Governance for LLMs, Agents & MCP

Your engineers are using AI. You know that. What you probably don't know is how many places it's running, who's triggering each call, what data is leaving for which provider, and how you would prove any of it to an auditor. Wallarm AI Hypervisor closes that gap and brings the same governance you already have for the rest of your stack to the LLM, agent, and MCP traffic flowing through your Kubernetes environment.

KNOW WHAT AI IS RUNNING

Stop discovering model providers by reading commit messages and auditing cloud bills. AI Hypervisor gives you a live inventory of every LLM provider, agent, MCP server, and tool your applications are actually using, updated continuously from real traffic. Anthropic, OpenAI, AWS Bedrock, Azure OpenAI, Gemini, Cohere, Mistral, and Together are recognized out of the box. Shadow AI surfaces next to the AI you officially approved.

KNOW WHO TRIGGERED IT

When a model returns something wrong, leaks something it shouldn't, or runs up a surprise bill, you need to know which end-user caused it. AI Hypervisor attributes every model call, agent step, and tool invocation back to the originating end-user across every internal service hop. Replay the exact prompt that produced a bad answer. Attribute LLM costs to individual users and teams. Reconstruct an AI-related incident timeline in minutes instead of days.

STOP THE BAD ONES IN REAL TIME

A misbehaving session shouldn't wait for the next deploy. Block a session, revoke a user, or stop a pattern instantly. No pod restart. No deploy cycle. No disruption to the workloads sharing the cluster. Enforcement is opt-in per namespace so platform teams can roll out one tenant at a time, while security gets a working off-switch for the rest of the estate.

PROVE IT TO AUDITORS

Whether the next conversation is EU AI Act review, SOC 2, an internal audit, or a customer security questionnaire, the evidence is already built. A current inventory of providers and tools, PII flow records traced from origin to model, an AI supply chain inventory with CVE enrichment, and full session replay. Compliance comes out of the same console your on-call team uses, not from spreadsheets assembled the week before.

ONE INSTALL, EVERY RUNTIME

AI Hypervisor covers Python, Node, Go, Java, and Ruby workloads in the same view, without application code changes, without an SDK to adopt, and without a per-language tool to operate. It supplements your existing APM, log pipeline, and SIEM rather than replacing them, ships connectors to the SOC tools you already run, and lives inside your own Kubernetes clusters.

WHO IT'S FOR

Leaders who own AI risk and need the same visibility, attribution, and audit posture for AI traffic that they have for everything else. Platform and AI infrastructure leaders running mixed- language workloads across many teams who want one consistent answer to "what is calling what, and is it safe?" Compliance and audit owners who need to prove what data left for which model provider, on whose behalf, on demand.