Hey folks, data_shipper_joe here! 👋 While I usually live in the data pipeline world, I've been neck-deep in ThreatConnect for a security analytics project. We needed a better way to visualize our progress on campaign attribution—something more dynamic than the static reports. The out-of-the-box widgets didn't quite cut it for our specific workflow, so I built a custom one.
The widget tracks the completion status of key attribution stages (like Indicator Analysis, Adversary Mapping, Tactic Confirmation) across multiple ongoing campaigns. It pulls data via the ThreatConnect API, aggregates the progress, and displays it as a sortable, color-coded dashboard right on the home screen.
Here's the core Python snippet I used to fetch and structure the data for the widget. It's a simplified version, but it shows the logic. I used the `requests` library and our internal tagging convention to group items.
```python
import requests
from typing import List, Dict
def get_campaign_progress(api_url: str, api_headers: Dict) -> List[Dict]:
"""Fetch groups tagged as 'campaign' and calculate stage completion."""
response = requests.get(f"{api_url}/api/v2/groups?tql=tag like 'campaign%'", headers=api_headers)
campaigns = response.json().get('data', {}).get('group', [])
progress_data = []
for campaign in campaigns:
# Example: Check for associated indicators (stage 1)
ind_resp = requests.get(f"{api_url}/api/v2/groups/{campaign['id']}/indicators", headers=api_headers)
indicators_linked = ind_resp.json().get('data', {}).get('indicator', [])
# Simplified logic: you'd have more complex checks for each stage
progress = {
'name': campaign['name'],
'stage_1_complete': len(indicators_linked) > 5, # Example threshold
'stage_2_complete': False, # Your logic for adversary mapping
'overall_progress': 0 # Calculate based on stages
}
progress_data.append(progress)
return progress_data
```
The widget itself is built with a simple HTML/JS frontend that consumes this processed data. It's been a game-changer for our team's visibility, letting us spot bottlenecks (like which campaigns are stuck in the "Adversary Mapping" phase) instantly.
I'm curious—has anyone else built custom visualizations or widgets on top of ThreatConnect? Would love to swap notes on API tips or if you've found clever ways to pipe this data into a lake (Snowflake, in my case) for longer-term trend analysis.
ship it
ship it
I appreciate the practical approach of bypassing the default widgets when they don't fit the workflow. However, the performance of your polling mechanism becomes critical as the number of tracked campaigns scales. You mentioned using `requests` for API calls; are you fetching the full dataset each time the widget refreshes? This can lead to unnecessary latency and load on the ThreatConnect API if you're not implementing incremental updates or at least a local cache with a TTL.
Consider structuring your data fetch to only retrieve groups modified since the last poll. Without seeing the rest of your TQL filter, that's a potential optimization. Also, for the aggregation logic, I hope you're performing the stage completion calculations client-side after fetching the raw data, rather than relying on the API to do the aggregation. Offloading that processing is usually more efficient and gives you finer control over the sorting and color-coding thresholds.
If you're displaying this on a shared home screen with auto-refresh, these considerations directly impact user-perceived latency. What's your current polling interval, and have you measured the 95th percentile response time from the API under load?
That's a solid approach using tags to filter the groups. We did something similar but found the tag-based filtering became a bottleneck as our taxonomy grew. The TQL queries on tags can get surprisingly slow if you have a complex hierarchy.
Instead, we ended up using custom attributes on the groups to track the attribution stage directly. It made the initial data model a bit more work, but the API calls to fetch and filter by those attributes are much faster. Your method is definitely quicker to prototype though, which is perfect for getting a proof-of-concept up and running.
Have you run into any issues with tag consistency across different analysts? That was our main pain point before we switched.
api first