I've been working with CrowdStrike's Falcon platform for a few years now, primarily for threat intelligence enrichment in our CI/CD and runtime environments. Like many teams, we initially subscribed to the full CrowdStrike Intel feed, piping all adversary and malware reports into our SIEM and security orchestration tools. While the volume of data was impressive, it created significant noise. Our incident response and deployment pipeline alerts were frequently triggered by threat actor activity that had no relevance to our industry, tech stack, or geographic footprint.
This week, during a review of our observability costs, I decided to dig deeper into the Falcon API and discovered a much more efficient approach. It turns out you can configure your intelligence subscriptions at a granular level, specifically selecting only the adversary groups (e.g., CARBON SPIDER, WORN SNAKE, HELIX KITTEN) or malware families that pose a tangible risk to your organization. This is managed through the `intel/entities/actors/v1` and `intel/entities/indicators/v1` API endpoints, allowing you to fetch and filter the intelligence universe before subscribing.
For example, our stack is primarily cloud-native (Kubernetes on AWS) with a focus on financial services. We have no need for intelligence on adversaries solely targeting manufacturing OT systems or specific regional government entities. By reviewing the actor metadata, we can build a targeted allowlist. Here's a simplified Python snippet using the `crowdstrike-falconpy` SDK that we now run as a periodic job to validate and update our subscriptions:
```python
# Define our criteria for relevant actors
RELEVANT_TARGET_INDUSTRIES = ["Financial", "Technology"]
RELEVANT_REGIONS = ["North America", "Global"]
def get_filtered_actor_ids():
from falconpy.intel import Intel
intel_api = Intel(client_id=CLIENT_ID, client_secret=CLIENT_SECRET)
# Fetch all actor entities
actors = intel_api.query_actors(limit=500)
filtered_ids = []
for actor in actors["body"]["resources"]:
details = intel_api.get_actors(ids=actor["id"])
actor_info = details["body"]["resources"][0]
# Filter logic
if any(ind in actor_info.get("target_industries", []) for ind in RELEVANT_TARGET_INDUSTRIES):
if any(reg in actor_info.get("regions", []) for reg in RELEVANT_REGIONS):
filtered_ids.append(actor_info["id"])
return filtered_ids
# Use these filtered IDs to configure your intel streams
```
The operational impact has been substantial. Our alert fidelity from tools like Falco or our custom pipeline security gates has improved because the underlying intelligence context is now directly applicable. Furthermore, this reduces the data processing load and storage costs in our observability pipeline, as we're no longer ingesting thousands of irrelevant indicators daily. For teams building automated response playbooks in Kubernetes environments, this focus is critical; you can tailor your container runtime policies and network rules to the specific TTPs of your actual threat landscape.
I'm curious if others in the community have implemented similar filtering strategies, and what criteria you've found most effective for prioritizing adversary intelligence. Particularly for those managing release engineering and deployment safety, how are you integrating this filtered intel into your canary analysis or post-deployment verification steps?
-jf