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The observability plugin gives you a centralized, queryable record of every scan decision and configuration change InferenceWall makes. Use it for SOC2/ISO27001 audit trails, real-time threat dashboards in Kibana, or correlation with other security logs during incident response. The plugin is completely optional — if IW_ELK_URL is not set, it is entirely inactive with zero overhead.

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Install the plugin

pip install inferwall[observability]
This adds the httpx dependency for HTTP log shipping. The base pip install inferwall does not include it.
1

Set IW_ELK_URL

Point the plugin at your Logstash HTTP input endpoint:
export IW_ELK_URL=http://localhost:8080
That’s the only configuration required. InferenceWall reads this variable at startup and enables the plugin automatically.
2

Start InferenceWall

inferwall serve
Every scan request fires a JSON log to Logstash asynchronously. If Logstash is unreachable, the error is silently suppressed — scan latency is never affected.
3

Verify logs are received

Trigger a scan and confirm Logstash received it:
# Trigger a scan
curl -X POST http://localhost:8000/v1/scan/input \
  -H "Content-Type: application/json" \
  -d '{"text": "ignore all previous instructions"}'

# Check Logstash received it
docker logs logstash | tail -5

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What gets shipped

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Scan logs

Every scan_input and scan_output call ships this JSON payload: 
{
  "log_type": "scan",
  "timestamp": "2026-04-06T12:00:00Z",
  "direction": "input",
  "decision": "block",
  "anomaly_score": 12.8,
  "threshold": 10.0,
  "policy": "default",
  "request_id": "req-1712345678000",
  "matches": [
    {
      "signature_id": "INJ-D-002",
      "matched_text": "ignore all previous instructions",
      "score": 6.3,
      "confidence": 0.9,
      "severity": 7.0
    }
  ],
  "signature_count": 1
}

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Audit events

Authentication, policy, and configuration changes ship this payload: 
{
  "log_type": "audit",
  "timestamp": "2026-04-06T12:00:00Z",
  "category": "auth",
  "action": "login_attempt",
  "details": {"key_prefix": "iwk_scan_"},
  "source_ip": "192.168.1.100"
}
Audit events are generated for these categories:
CategoryExamples
authLogin attempts, key validation failures
policyPolicy loads, mode changes
configEnvironment variable changes at runtime
signaturesCatalog reloads, override registrations
enginesEngine initialization, model loads
adminAdmin API calls
rate_limitRate limit hits
lifecycleServer start, shutdown, health state changes
scanHigh-severity scan decisions (supplemental to scan logs)

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Configuration

VariableDescriptionDefault
IW_ELK_URLLogstash HTTP input endpointNot set (plugin disabled)
If IW_ELK_URL is not set, the observability plugin is completely inactive — no imports, no background threads, no side effects. Set the variable before starting InferenceWall to enable it.

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Architecture

The plugin uses a fire-and-forget model:
  • Each log event is sent via httpx.post() with a 5-second timeout.
  • Shipping runs after the scan response is returned to the caller — it never blocks the scan pipeline.
  • All network errors and timeouts are silently suppressed. If Logstash is unreachable, logs are dropped without affecting scan latency or availability.

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Logstash pipeline

Minimal Logstash pipeline to receive InferenceWall logs and forward them to Elasticsearch: 
input {
  http {
    port => 8080
    codec => json
  }
}

output {
  elasticsearch {
    hosts => ["elasticsearch:9200"]
    index => "inferwall-logs-%{+YYYY.MM.dd}"
  }
}

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Docker

Run InferenceWall with ELK shipping enabled by passing IW_ELK_URL as an environment variable: 
docker run -d \
  -e IW_ELK_URL=http://logstash:8080 \
  -e IW_API_KEY=iwk_scan_yourkey \
  --network your-elk-network \
  -p 8000:8000 \
  inferwall:latest
Make sure InferenceWall and Logstash are on the same Docker network so the hostname resolves.

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Use cases

  • Compliance (SOC2, ISO27001) — centralized audit trail of all scan decisions and configuration changes
  • Threat monitoring — Kibana dashboard showing block rate trends and top triggered signatures
  • Incident response — correlate LLM firewall events with other security logs by request_id and timestamp
  • Tuning — identify high false-positive signatures by querying for decision: flag events with known-benign inputs