For SOC Teams

REQUEST A DEMO

The gap

Inventorying AI assets isn't securing them. It's just naming them.

Shadow AI workloads ship faster than security can catalog them.

A model's config tells you nothing about what it actually does.

Agents call APIs, read data, and act on systems with no audit trail.

Sensitive data flows into models that weren't approved to see it.

What it is

Posture management for AI that's actually running

Discovery, classification, and risk assessment for every AI workload in your cloud, grounded in the execution paths your models and agents take in production.

→ Not just what AI you have. What your AI is doing.

REQUEST A DEMO

Full AI inventory, including shadow workloads

Every model endpoint, inference service, agent, vector store, and training pipeline running in your cloud. Discovered at runtime, no manual registration.

End-to-end execution path visibility

Trace every call from user input through agents, models, tool use, and data access. See what your AI is touching, what data it's pulling, and what it returns.

Sensitive data flow into AI, monitored

PII, regulated data, and proprietary IP tracked as it moves into prompts, training pipelines, and vector stores. Policy violations surface as they happen, not on the next audit.

Risk scored against actual AI behavior

Misconfigurations, exposure, and policy violations ranked by what the workload is really doing, not what its config claims. The 500-finding AI report becomes the five that matter.

How it works

Discover. Observe. Govern.

01.

AI Privileged
Access

AI agents increasingly operate as automation systems with access to APIs, cloud services, internal applications, and sensitive data. If manipulated or compromised, attackers may indirectly access infrastructure and critical systems through those agents — creating new pathways into cloud environments.

02.

Visibility Lost at the Prompt

Most AI security tools focus on prompt injection detection, model safety, and LLM guardrails. While these approaches analyze text interactions with models, they rarely reveal what actually happens when AI systems execute actions across production infrastructure.

03.

Runtime Blind Spots in the Cloud

To truly secure AI workloads, organizations must understand how AI interacts with cloud infrastructure. Without runtime visibility, security teams cannot see how AI-driven activity moves across services, APIs, and data stores or understand the potential impact on critical systems.

Step 1

Discover every AI workload

Continuous runtime discovery surfaces every model endpoint, inference service, agent, and data pipeline running in your cloud. Including the ones your platform team didn't tell you about.

Step 2

Observe execution paths

Trace the full path of every AI interaction, from user prompt through agent reasoning, tool calls, model invocation, and data access. The story of what your AI actually did, captured as it runs.

Step 3

Govern with real evidence

Policy violations, data exposure, and risky behavior surfaced and prioritized by what's actually happening in production. Evidence ready for governance reviews, audits, and incident response.

Context

AI security that goes beyond the inventory list.

First-generation AI-SPM stops at discovery and config checks. That's table stakes. The harder question, the one boards and regulators are starting to ask, is what your AI is actually doing in production. RoonCyber answers it.

EXECUTION PATH VISIBILITY

The behavior layer most AI-SPM tools can't reach.

User prompt to model output, traced end-to-end
Agent tool calls and external API interactions captured
Data access by inference services and training pipelines
Anomalous behavior detected against learned baselines

For the full technical story, see our AI Workloads product page.

GOVERNANCE-READY EVIDENCE

The artifacts your AI governance program actually needs.

Which models are running, where, and who owns them
What data each model has access to and uses
How agents interact with downstream systems and APIs
Where policy violations and risky behavior happen

“An AI inventory tells you what models exist. That's the easy half. The hard half is showing what those models did when no one was looking, and that's what runtime visibility is for.”

What changes

Confidence in your AI estate. Evidence for every conversation.

15x

Faster AI incident response

Execution path evidence cuts investigation time from days of forensic stitching to minutes of timeline review.

90%

Fewer false positives in the AI queue

Risk scored by actual behavior, not theoretical config exposure. Findings reflect real production risk, not catalog noise.

50%

Lower cost of AI security

One platform for AI workload discovery, behavior monitoring, and governance evidence. No standalone AI security tool to buy or babysit.

Built for real AI operations

Discover shadow AI workloads and unowned endpoints

Track sensitive data flow into models and prompts

Audit agent tool use and downstream system access

Detect runtime anomalies in inference behavior

Generate evidence for AI governance reviews

Runtime security should be exactly how it sounds - always active and everywhere within your organization.

AI Security Posture Management

See the full execution path of every AI workload.