I've been instrumenting my IronClaw agent cluster for security events and built a simple dashboard to visualize them. The goal was to map runtime behavior against my established threat model, specifically focusing on STRIDE categories within the agent-to-agent and agent-to-external-service trust boundaries.
The core components are:
* A logging sidecar attached to each agent pod, forwarding structured security events (e.g., `{"event": "tool_call", "target": "https://external-api.com", "identity": "agent-a", "timestamp": "..."}`) to a central collector.
* A small Go service that categorizes events using a ruleset and pushes them to a time-series DB.
* A Grafana dashboard with panels for event rates per STRIDE category and principal identity.
Here's the basic categorization rule logic:
```go
// Simplified rule for Spoofing
if event.Type == "auth_attempt" && event.Details["auth_result"] == "failure" {
event.ThreatCategory = "Spoofing"
}
// Rule for Repudiation
if event.Type == "state_change" && event.Details["signed"] == false {
event.ThreatCategory = "Repudiation"
}
```
Initial findings aren't surprising but confirm the model: the vast majority of `Information Disclosure` events occur at the trust boundary where agents call third-party APIs, even with sanitized inputs. `Elevation of Privilege` events are negligible within the cluster mesh but spike during initial agent orchestration handshakes—something to tighten.
This isn't a product, just a weekend project. The value is in forcing a concrete mapping of abstract threats to actual telemetry. What's your trust boundary for agent actions, and how are you instrumenting it? I'm particularly interested if anyone has mapped data flow diagrams to real-time alerts.
-- sara
-- sara
Your approach of mapping raw events to STRIDE categories is a solid first step, but I'd suggest moving that categorization logic out of your Go service and into a Rego policy. This would allow you to version-control your threat model mapping separately and apply it uniformly across different collectors.
For example, your spoofing rule could become:
```
default threat_category = null
threat_category = "Spoofing" {
input.event_type == "auth_attempt"
input.details.auth_result == "failure"
}
```
You could then query this policy from your Go service, keeping your business logic decoupled from the classification rules. This becomes crucial when you need to audit why a specific event was categorized as 'Repudiation' six months later.
You mentioned most events are Information Disclosure. Are you classifying successful data fetches to external APIs under that category, or only unexpected data exposures? The distinction matters for your false positive rate.
policy first