Device diversity
Each operating system exposes different signals and imposes different collection limits.
Endpoint AI security
Start AI security where agent work begins.
The endpoint can preserve origin context that cloud-side telemetry never receives. AxLoop's production-ready foundation begins with native macOS discovery.
Direct definition
What is endpoint AI security?
Endpoint AI security focuses on supported evidence for AI applications, agents, models, services, and configurations on enterprise-managed devices.
AxLoop knowledge base
The visibility problem
Evidence must come before control.
Local execution
Models and agents can run locally rather than in centrally observed cloud systems.
External communication
Applications and devices may communicate with external AI infrastructure before central systems can add context.
How AxLoop helps today
Discover from the endpoint outward.
AxLoop identifies supported AI applications, agents, local models, runtimes, and MCP configuration evidence. Each finding keeps installed, configured, running, and independently verified states distinct.
- Identify supported software and configuration evidence
- Preserve device and user scope
- Monitor meaningful state changes
Questions teams ask
- Q1Which supported endpoints contain AI software?
- Q2What evidence shows a local model or agent is running?
- Q3Which relationships need separate verification?
Common questions
Answers, briefly.
- How is endpoint AI security different from EDR?
- EDR looks for malicious behavior across all software. Endpoint AI security focuses on understanding legitimate and unmanaged AI software: what it is, what it can reach, and who owns it. The two are complementary.
- Which devices does AxLoop support?
- The strongest verified implementation is native macOS on Apple Silicon and Intel Macs. The broader product direction includes Windows, Linux, iOS, Android, and servers, with each platform limited to signals its operating system safely exposes.
- What does AxLoop never collect from endpoints?
- AxLoop is designed not to collect prompts, model responses, source code, file contents, or secrets. It works from metadata such as software identity, configuration presence, process information, and connection context.
- How is endpoint evidence delivered?
- Findings are buffered locally in a durable outbox and exported using OpenTelemetry (OTLP), so evidence survives connectivity gaps and fits existing observability and security pipelines.
Agent Interaction Observability