Hi everyone, I could really use some guidance on calibrating Claw-Scout. I’m fairly new to setting up data pipelines and I’ve hit a wall with this security scanning tool.
We’re using it to monitor our Airflow DAGs that pull data from a few external sources—Stripe, a weather API, and a shipping API. The problem is that Claw-Scout is flagging *every single API call* as a ‘high risk’ data egress. I understand being cautious, but this feels like overkill and is drowning out any potentially real issues.
Our current config is pretty much the out-of-the-box setup. In our `claw-scout-config.yaml`, the relevant part looks like this:
```yaml
risk_detection:
third_party_domains:
sensitivity: high
flagged_patterns: ["api.*", "*.com", "*.io"]
data_volume_threshold: 1MB
```
This is causing a flood of alerts in our Slack channel. My questions are:
* What’s a realistic starting point for `sensitivity` for common third-party SaaS APIs? Should I set up a tiered list?
* Is the `flagged_patterns` list too broad? It seems like `["api.*"]` alone might catch everything.
* How are you all handling whitelisting known, trusted endpoints? Do you do it by domain, or by a specific DAG/task pattern?
I’m a bit overwhelmed trying to tune this without accidentally silencing a real threat. Any concrete examples of your working configurations would be a lifesaver. I just want it to distinguish between a normal call to Stripe and something truly suspicious.
Thanks in advance for any help you can offer
null
Your flagged patterns are the main problem. `["api.*", "*.com", "*.io"]` is absurdly broad - you're literally telling it to flag every .com and .io domain, which is everything. No wonder you're flooded.
Drop the generic TLD patterns immediately. Start with an explicit allow list of your known vendor domains under `trusted_domains`. For example:
```yaml
risk_detection:
third_party_domains:
sensitivity: medium
flagged_patterns: []
trusted_domains: ["api.stripe.com", "api.weatherapi.com", "api.shipengine.com"]
```
Set sensitivity to `medium` and tune the data volume threshold based on your actual data profile - 1MB is tiny for many legitimate jobs. Bump it to 10MB or 50MB as a starting point, then adjust after a week of logs. The goal is to catch anomalous spikes, not routine operation.
Show me the benchmarks
Agreed on the patterns. That config would flag our own internal dashboard.
One thing to check: some of those trusted domains might have CDN subdomains that aren't "api." If your weather service uses cdn.weatherapi.com for static assets, you'll get new alerts. You'll need to add those subdomains too, or use a broader wildcard like "*.weatherapi.com" if you trust their whole infrastructure.
Also, bumping the data volume threshold is key. But watch your logs for a few days after changing it. If every job sends exactly 12MB, that's your new normal. You're looking for the outlier at 250MB.
Exactly, your flagged patterns are the root issue. Starting with `["api.*", "*.com", "*.io"]` is like setting off a fire alarm every time someone uses a door. It's not just broad, it's essentially telling Claw-Scout that the entire commercial internet is suspicious.
I'd suggest flipping your approach entirely. Instead of a blocklist via `flagged_patterns`, start with an empty list there and build a `trusted_domains` list for your known vendors. For your use case, it might look like this:
```yaml
trusted_domains:
- "api.stripe.com"
- "api.weatherapi.com"
- "api.shipengine.com"
- "cdn.shipengine.com" # because user1303 is right, CDNs will trip you up
flagged_patterns: []
sensitivity: medium
```
Set sensitivity to `medium` for starters. High is really for untrusted, net-new domains you've never seen before. Since you know these SaaS providers and have a contractual relationship with them, `medium` is more appropriate. It'll still alert if, say, a DAG suddenly starts sending 100x more data to Stripe, but it won't scream about every single daily sync.
Also, bump that `data_volume_threshold` way up. 1MB is nothing for API responses nowadays. Try 20MB or even 50MB as a baseline, then watch your logs for a week to see what "normal" actually looks like for your pipelines. The goal is to find the true outliers.
Automate all the things.
You've hit the nail on the head - those flagged patterns are definitely the culprit. Starting with a list of trusted domains is the right move, like others have said.
I'd add a small tip from my own headaches with similar tools: you might want to temporarily set sensitivity to `low` for a week. It sounds counterintuitive, but it lets you see *everything* that's trying to connect without triggering the alert storm. You can log those domains, build your proper trusted list from that real data, and then bump the sensitivity back to medium. It's a cleaner way to discover any weird subdomains or redirects you didn't anticipate.
For your last question about whitelisting by domain vs DAG, I always go by domain. If you trust api.stripe.com for one DAG, you should trust it for all of them, otherwise your security policy gets really messy.
test everything twice
Your flagged patterns are definitely the problem - `["*.com", "*.io"]` is effectively blocking the commercial internet. While others have suggested moving to a `trusted_domains` list, which is correct, I'd recommend a more structured approach for scaling.
Instead of just listing domains, consider using a separate YAML file for your trusted vendor list that includes metadata. This lets you document why each domain is trusted and categorize sensitivity. For example:
```yaml
vendors:
- domain: "api.stripe.com"
business_justification: "payment processing"
risk_tier: "low"
subdomain_wildcard: false
- domain: "*.weatherapi.com"
business_justification: "weather data for logistics"
risk_tier: "medium"
subdomain_wildcard: true
```
You can then write a small script to generate the actual `trusted_domains` array from this list. This becomes crucial when compliance asks for your third-party vendor risk assessment - the data is already structured.
Set sensitivity to `medium` and increase your volume threshold substantially. 1MB is appropriate for credential exfiltration detection, not for bulk data transfers. Start at 100MB for your use case.