Observability layer
Collects normalized runtime telemetry across models, agents, and tools.
LandmarkSignal Radar
Radar is designed to help teams monitor AI activity, identify outlier behavior, and prioritize risk signals before they turn into operational incidents.
What it does
Where it sits
Model calls, agent actions, tool events
Normalization, correlation, risk scoring
Prioritized queues and operational context
Core components
Collects normalized runtime telemetry across models, agents, and tools.
Links related events across sessions to surface high-value patterns.
Applies configurable scoring to prioritize analyst investigation queues.
Threats addressed
Deployment patterns
Radar is designed to support self-hosted, private cloud, and hybrid deployment patterns.
Deployment depth and packaging can follow roadmap sequencing by environment.
What it is not
Radar is focused on AI-runtime visibility and AI-specific risk correlation.
It is not intended to replace every infrastructure-wide logging or SIEM workflow.
Use cases and audience
Security operations and AI platform teams use Radar to triage unusual behavior, prioritize investigations, and improve runtime decision quality.
Example workflow: review high-risk signal clusters, validate policy context, and route findings into incident handling.
Call to action
Request a product briefing to review representative scenarios and deployment options for your environment.