How to Integrate Dark Web Monitoring API with Your SIEM

Your SIEM already holds authentication, endpoint, and network events. An external threat feed is easier to investigate when analysts can review it beside those records.

A dark web monitoring API can place matching source records in that same workflow. Analysts can then compare a record with internal evidence before opening or escalating an incident.

The examples below cover Splunk, Microsoft Sentinel, QRadar, and Elastic. Test the field mapping and delivery path in your own environment before sending production data.

Why SIEM Integration Matters

Analysts lose time when they must check another dashboard and copy records by hand. A SIEM integration keeps the source record and the related internal event in one investigation queue.

SIEM integration solves this by:

• Centralizing alerts – Dark web threats appear alongside internal security events
• Enabling correlation – Match exposed credentials against authentication logs
• Automating response – Trigger playbooks when threats are detected
• Improving metrics – Track mean time to detect (MTTD) for external threats

Integration Architecture Overview

Providers commonly expose one or both of these integration patterns:

1. Webhook (Push)

The provider sends a request to a configured endpoint after it creates an eligible alert. Measure delivery time from collection through SIEM ingestion; a webhook alone does not guarantee real-time delivery.

2. Polling (Pull)

Your integration queries the API on a schedule. Polling gives you control over ingestion frequency and cursor handling, but you must account for rate limits and failed runs.

Splunk Integration

Splunk's HTTP Event Collector (HEC) accepts JSON events over HTTPS. Create a token, then test the polling script against a dedicated index.

Step 1: Create HEC Token

In Splunk, navigate to Settings → Data Inputs → HTTP Event Collector. Create a new token with a dedicated index for dark web data.

Step 2: Configure Webhook

Point your dark web monitoring API webhook to your Splunk HEC endpoint:

https://your-splunk:8088/services/collector/event
Headers: Authorization: Splunk YOUR_HEC_TOKEN

Step 3: Create Python Polling Script

For polling integration, use this Python script as a starting point:

import requests
from datetime import datetime, timedelta, timezone

DARKWEB_API = "https://platform.adversemonitor.com/api/v1/threats"
SPLUNK_HEC = "https://your-splunk:8088/services/collector/event"
API_KEY = "your-api-key"
HEC_TOKEN = "your-hec-token"

# Fetch recent threats
response = requests.get(
    DARKWEB_API,
    headers={"Authorization": f"Bearer {API_KEY}"},
    params={"since": (datetime.now(timezone.utc) - timedelta(hours=1)).isoformat()},
    timeout=10,
)
response.raise_for_status()

# Send to Splunk
for threat in response.json()["data"]:
    event = {
        "event": threat,
        "sourcetype": "darkweb:threat",
        "index": "darkweb_intel"
    }
    requests.post(
        SPLUNK_HEC,
        headers={"Authorization": f"Splunk {HEC_TOKEN}"},
        json=event,
        timeout=10,
    ).raise_for_status()

Step 4: Build Correlation Searches

Create Splunk searches that correlate dark web intel with internal data:

index=darkweb_intel sourcetype="darkweb:threat"
| spath path=victims.domains{} output=observed_domain
| mvexpand observed_domain
| join observed_domain [search index=authentication | rename src_domain as observed_domain | stats count by observed_domain]
| where count > 0
| table _time, category, title, risk_level, observed_domain, count

Microsoft Sentinel Integration

Microsoft Sentinel offers multiple integration options for dark web APIs.

Option 1: Logic Apps

Create a Logic App that polls the API and ingests data into your Log Analytics workspace:

1. Create a new Logic App with a Recurrence trigger (every 15 minutes)
2. Add HTTP action to call the dark web API
3. Parse JSON response
4. Send data to Log Analytics using the Data Collector API

Option 2: Azure Functions

For more complex processing, use an Azure Function:

import azure.functions as func
import requests

def main(timer: func.TimerRequest) -> None:
    api_response = requests.get(
        "https://platform.adversemonitor.com/api/v1/threats",
        headers={"Authorization": "Bearer YOUR_API_KEY"},
        params={"limit": 50},
        timeout=10,
    )
    api_response.raise_for_status()

    # Map these records to the columns in your custom Log Analytics table.
    log_analytics_client.send(
        log_type="DarkWebThreats",
        body=api_response.json()["data"]
    )

Step 3: Create Analytics Rules

After mapping the returned fields to your custom table, query those same fields in Sentinel:

DarkWebThreats_CL
| where TimeGenerated > ago(1h)
| where isnotempty(category_s)
| project TimeGenerated, title_s, category_s, risk_level_s, published_at_t

QRadar Integration

IBM QRadar accepts dark web data through its REST API or Universal Cloud Connector:

Using Log Source Extension

1. Create a custom log source type for dark web events
2. Configure a Universal REST API connector
3. Map API response fields to QRadar properties
4. Create custom rules for threat detection

Elastic Security Integration

Elastic makes ingestion simple with Logstash or Elastic Agent:

Logstash Configuration

input {
  http_poller {
    urls => {
      darkweb => {
        url => "https://platform.adversemonitor.com/api/v1/threats"
        headers => { "Authorization" => "Bearer YOUR_API_KEY" }
      }
    }
    schedule => { cron => "*/5 * * * *" }
  }
}

output {
  elasticsearch {
    hosts => ["https://your-elastic:9200"]
    index => "darkweb-threats-%{+YYYY.MM.dd}"
  }
}

Best Practices for SIEM Integration

1. Normalize Data Fields

Map dark web API fields to your SIEM's common information model. This enables correlation with other data sources and consistent alerting.

2. Set Appropriate Severity Levels

Not all dark web mentions are critical. Configure severity based on:

• Critical: Active ransomware targeting, credential dumps with your domain
• High: Mentions on initial access broker forums
• Medium: Industry-related threats, general chatter
• Low: Historical references, resolved incidents

3. Avoid Alert Fatigue

Configure deduplication and aggregation. Multiple mentions of the same threat shouldn't generate separate incidents.

4. Automate Response

Connect dark web alerts to your SOAR platform. Automate actions like:

• Forcing password reset for exposed credentials
• Blocking IPs associated with threat actors
• Creating tickets for investigation
• Notifying affected business units

5. Maintain API Health

Monitor your integration for failures. Set up alerts for API errors, authentication failures, and data gaps.

Troubleshooting Common Issues

API Rate Limiting

If the API returns 429, pause the poller and use its rate-limit headers to decide when to retry. Add bounded exponential backoff for transient errors.

Data Format Mismatches

Ensure your SIEM parser handles the API's JSON structure. Test with sample data before production deployment.

Missing Historical Data

Use the documented since parameter and response cursor to collect the history available to your subscription. Store the last successful cursor so a failed run does not create a gap.

Measuring Integration Success

Track these metrics to validate your integration:

• Mean Time to Detect (MTTD): How quickly do dark web threats appear in your SIEM?
• Alert Volume: Are you getting actionable alerts without noise?
• Correlation Rate: What percentage of dark web alerts match internal events?
• Response Time: How quickly does your team act on dark web intelligence?

Conclusion

A SIEM integration gives analysts one place to compare an external source record with internal telemetry. Correlation rules still need explicit field mappings, thresholds, and owners.

The HTTP request is the small part. Plan time for parser tests, retry behavior, correlation rules, and an analyst runbook.

Start with one narrow query and review the returned evidence. Expand the integration only after the team can measure false matches, ingestion failures, and investigation time.