Platform / AI Detection & Response
While it runs
AI Detection & Response. Agents are endpoints now.
Your EDR watches processes, files, and network calls. An agent's decision to reach for a credential is none of those. Starseer monitors the layer where agents actually act.
The problem
Traditional EDR was built for software that follows a known path.
Models and agents do not. They make autonomous decisions and take actions from learned behavior, which is why threats to them leave none of the traces your security stack matches on.
Why your existing stack cannot see it
01
There is no process to watch
An agent choosing its next action is not a syscall, a file write, or a suspicious binary. Nothing in a conventional endpoint model accounts for a system that decides.
02
The threats produce no signature
Backdoored models, drifting behavior, covert capability, substituted weights. None of these generate the artifacts signature-based tooling was designed to find.
03
You may not know the model is there
Models get deployed without security review. Shadow AI is already running on endpoints and servers your inventory does not list.
How it works
Detection, response, and the record, on one loop.
Every finding tunes the next detection, and the validated baseline from the vulnerability scan is what runtime measures against.
01
Monitor
A behavioral baseline per model and agent, built from activations and decision-to-action chains rather than output sampling.
02
Detect
Rules fire on what the agent was computing and about to do, not on how the response was phrased.
03
Contain
Throttle or isolate the session in milliseconds, with prompt lineage preserved from origin to final input.
04
Tune
The finding feeds back into the detection, so each incident leaves the system harder to repeat against.
Your analyst opens a case file, not just an alert.
When a probe fires on a model's internal state, the prompt, the tool call, and the full application trace are already on the same timeline.
Nothing to reconstruct. Nothing to correlate at 2am. The investigation is assembled before anyone opens it, and the session is already contained.
prompt received
tool call · shell.exec
trace · app to gateway
probe · model activations
The probe fired on the model's internal state. Everything else was already on the timeline. That is the case file.
What it catches
Six failures that leave no conventional trace.
Each is detected from the model's own internal state and the actions it took, not from the text it returned.
Compromised agents
Prompt injection and tool abuse that reach a real action. The detection fires on what the agent was about to do, before the call lands.
Behavioral drift
Degradation measured against the approved baseline, so a model getting quietly worse at your task surfaces before your users report it.
Runtime substitution
Model identity verified as it executes, flagging weights that were swapped, tampered with, or silently updated after approval.
Shadow AI
Discovers and catalogs active models and agents across endpoints, servers, and edge infrastructure, including those deployed with no security review.
Covert capability in use
Identifies a model exercising capability its documentation never described, at the moment it is exercised rather than in a later audit.
Unreconstructable incidents
Continuous decision-level records mean root cause is read, not inferred, and the evidence is already in a form an auditor accepts.
In your SOC
Arrives in the workflow your team already runs.
Findings land as traces, logs, and alerts in the tools your analysts already have open. Nothing new to watch, and no separate console to staff.
Detections as code
YARA-X rules mapped to MITRE ATLAS, versioned and validated in CI like the rest of your detection content.
Feeds your existing tools
OpenTelemetry traces, SIEM-ready logs, and API alert forwarding into SOAR for automated or guided response.
Covers autonomous systems
Drones, robotics, manufacturing automation, and medical devices, where an AI failure is a physical outcome rather than a security event.
Detection engine
YARA-X rules, versioned like code
Forwards to
SIEM, SOAR, OpenTelemetry
Scope
Any agent framework, any model endpoint, cloud to air-gapped edge
Evidence for
MITRE ATLAS, NIST AI RMF, ISO 42001, EU AI Act, OWASP AI Top 10
Works with
See what your agents are actually doing.
A working session against one of your own agent workloads, or four minutes with the diagnostic.