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See Wallarm's API Protection In Action
Talk to an Expert
Solution · Observe
Observe.
Deployment tells you what's running. Wallarm tells you what it's doing.
A GuardDuty finding fires on an AI workload. The CISO has no context. Which agent? Who triggered it? What data did it touch? What did it send out? Wallarm AI Hypervisor closes that gap. It instruments every AI workload at runtime, captures every outbound call with user-level attribution, and surfaces sensitive data the moment it moves through a pipeline.
Runtime · no code changes
User-level attribution
eBPF · in-cluster
The Problem
Detection without context is just noise.
When an alert fires on an AI workload, the next five questions are the ones nobody can answer.
The trace breaks at every hop
A user's request crosses three internal services before it reaches a model provider. The internal services don't forward identity. By the time the call exits to Anthropic or Bedrock, the trace is gone. No way to stitch "Alice's request" to the outbound call it produced.
Sensitive data leaves before anyone notices
PII flows through AI pipelines at speed. Macie catches it in S3, after it has already moved through five stages. By then it's in a system of record, in a log, and potentially in a model provider's training data. The team finds out at the breach review.
Agent behavior is a black box
The agent did something. The team can see the outcome, not the chain. Which tool did it call? What did the prompt say? What did the model return? Reconstruction takes hours of log scraping, and the prompt is usually not in any log to begin with.
Outcomes
What changes with Wallarm.
Every AI call, attributed to the user who triggered it
The CISO can answer "this agent, on whose behalf, did what" instead of "an agent did this." Cost attribution, audit, and incident response work with the same primary key: the human identity that started the chain.
Sensitive data exposure surfaces in flight
Credit cards, SSNs, passport numbers, dates of birth, email addresses, API keys, and JWT tokens are detected as they move through AI pipelines. Before they reach a model provider. Before they reach S3. Before they reach the next compliance report.
One session, one timeline, end to end
A single view assembles every step a user's request triggered: agent invocations, model calls, tool invocations, PII detections, guardrail events, policy blocks. Incident response works from a reconstruction, not a five-tab log search.
AWS findings, made actionable
GuardDuty, Inspector, IAM Access Analyzer, Macie, and AWS Config findings land on the relationship graph node they affect, with the runtime context AWS-native tools cannot see. Plain-language enrichment makes the alert queue something analysts act on, not just look at.
Capabilities
How Wallarm does it.
1
Runtime · no code
Runtime instrumentation, no code changes
Instrumentation begins within seconds of pod start. Python, Node, Go, Java, Ruby, and generic container workloads are all covered, with no application code changes required. Patented non-invasive analysis means the application never gets touched.
AI Hypervisor →2
Attribution
User-to-call attribution across hops
JWT subject and trace context propagate across service boundaries. Where internal services don't forward identity, kernel Thread ID correlation stitches calls back to the originating session. The chain holds even when the engineering team didn't build it to.
AI Hypervisor →3
TLD Capture
TLS-decrypted LLM call capture
Outbound LLM calls are decoded in-cluster via eBPF on the data path. No forward proxy to maintain. No cert pinning to fight. No HTTP_PROXY rewiring across hundreds of services. The full request and response body is reconstructed, indexed, and searchable.
AI Hypervisor →4
PII inline
PII and canary detection inline
PII patterns and customer-defined canary tokens are detected and masked in captured prompts and responses. Every detection ties back to the originating session, with a Data Tracks view that traces propagation across agents, tools, and providers.
AI Hypervisor →5
Waterfall
Session waterfall and Debugger
Per-session timelines show every agent step, model call, tool invocation, and policy event. The Debugger steps through the exact request that tripped a guardrail, with link-back to the asset, the user, and the canary or PII record involved.
AI Hypervisor →6
AWS on graph
AWS Security Hub on the graph
Findings from GuardDuty, Inspector, IAM Access Analyzer, Macie, and AWS Config sync every five minutes. Each finding lands on the relationship graph node it affects, with plain-language enrichment alongside the raw text for audit.
Infrastructure Discovery →The AI Control Loop
Discover. Observe. Enforce. Govern.
Not four separate products. One platform, one continuous loop where each step feeds the next automatically.
Observe
You Are Here
See what the AI is actually doing at runtime, attributed back to the user.
GET A DEMO
Ready to see what
your AI is actually doing?
"We didn't need to change anything in the application deployment infrastructure. The installation itself is easy and straightforward."
Konstantin Golubitsky · CTO, Xsolla
Sixty seconds from Helm install to first captured session. No application code changes.