Secure what AI actually does — not just what it says.
AI agents now invoke tools, call APIs, and reach data stores across your cloud. RoonCyber watches the execution layer in real time — so a compromised agent can't quietly become a path into your infrastructure.
Agents operate as privileged automation — holding keys to APIs, data stores, and internal services.
Prompt monitoring is a blind spot.
Scanning text for suspicious patterns misses what matters: what the agent does once it acts.
AI activity moves through the cloud unseen.
Without runtime visibility, AI-driven actions traverse your environment — carrying risk no scanner catches.
What it is
Runtime security for the AI execution layer.
RoonCyber observes AI workloads where the real risk lives — the layer where agents invoke tools, touch data, and trigger actions across your cloud.
→ Secure what AI actually does — not just what it says.
Agent behavior and tool invocation
Every tool an agent calls, every workflow it triggers — captured as it executes.
AI-driven data access
Which data stores an agent reads or writes — and whether sensitive data is in play.
Container and cloud activity
The processes, syscalls, and network connections AI systems set off in production.
Autonomous workflows across the cloud
Where AI-driven actions reach — and how far an autonomous chain of events can go.
Why runtime
AI systems don't stop at the prompt.
A subtle prompt injection may slip past text analysis entirely. What matters is what happens next — and that only exists in runtime telemetry.
AI SECURITY TODAY
Prompt monitoring
Flags suspicious text. But attackers rarely use obvious instructions — and a clean-looking prompt tells you nothing about the data exfiltration that follows.
What most AI Security tools see
AI SECURITY NEEDED
Runtime workload security
Kernel-level sensors capture system calls, network connections, file access, and process execution — ground truth about what the agent actually did, not what it claimed.
How it works
Observe the execution layer. Correlate with the cloud. Contain in real time.
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
Runtime detection & validation
Kernel-level sensors observe what AI workloads actually execute — processes, syscalls, file access, network traffic — turning hypothetical threats into observed behavior.
Step 2
Attack path context
Runtime events are correlated against a live cloud inventory — compute, storage, services, data, identities — to map exactly where AI-driven activity can move.
Step 3
Automatic containment
Because monitoring sits at the system level, RoonCyber can block connections, kill processes, pause containers, and revoke tokens — before the damage spreads.
Context
Connect AI activity to real business risk.
An agent in a vulnerable container with exposed credentials is a different risk than one in a locked-down environment. RoonCyber correlates runtime behavior with cloud context to show which one you're looking at.
AI ATTACK PATHS
See how a compromised agent could move.
Which services, containers, and APIs the agent can reach
How AI-driven activity traverses layers of infrastructure
The full exploit path from prompt to sensitive data
Vulnerabilities and misconfigurations along the way
BLAST RADIUS
Understand the true scope of a compromise.
The blast radius of a compromised AI agent
Which resources are reachable and which data is exposed
Downstream systems carried along an autonomous workflow
Runtime threats translated into dollars and risk scores
“An AI agent is effectively a privileged automation script. If it's compromised, it behaves like an insider threat — and the only place you'll see that is the runtime layer.”
Response
From AI monitoring to AI defense.
Most AI security tools only generate alerts — and by the time an alert appears, the damage is already underway.
RoonCyber detects abnormal AI behavior — an agent that suddenly scans internal services or reaches for secrets — and acts on it at the system level, automatically, while triggering an investigation.
Agent behavior and tool invocation
Every tool an agent calls, every workflow it triggers — captured as it executes.
AI-driven data access
Which data stores an agent reads or writes — and whether sensitive data is in play.
Container and cloud activity
The processes, syscalls, and network connections AI systems set off in production.
Autonomous workflows across the cloud
Where AI-driven actions reach — and how far an autonomous chain of events can go.
What changes
One unified platform — cloud, application, and AI activity together.
15x
Faster MTTR
Runtime context turns AI incidents from guesswork into observed, traceable events.
90%
Fewer false positives
Runtime context turns AI incidents from guesswork into observed, traceable events.
45–50%
Lower TCO
Runtime context turns AI incidents from guesswork into observed, traceable events.
Built for AI in production
Contain AI threats automatically, in real time
Score AI-driven risk in business and dollar terms
Map AI attack paths across multi-cloud and Kubernetes
Detect the real consequences of prompt injection
Spot compromised agents behaving like insider threats
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