Hey everyone,
I've been diving deep into integrating external threat intelligence into our infrastructure provisioning pipelines lately. We're using Terraform to spin up cloud resources, and I've been experimenting with using services like Recorded Future (RF) to make dynamic security decisions based on domain reputation—think conditionally creating Route53 records, allowing/blocking egress rules, or tagging resources.
One of the key data points we're testing is the **domain risk score**. It's incredibly tempting to use this in a `locals` block or a module to automate decisions. However, before we bake this into our `main.tf` for anything beyond logging, I need to get a solid handle on accuracy and nuance.
So, my core question: **Has anyone done a systematic, hands-on comparison between Recorded Future's domain risk scores and something like VirusTotal's domain reputation?**
I'm particularly curious about the operational aspects:
* **Score Discrepancies:** Have you seen cases where RF flags a domain as "high risk" but VirusTotal reports it as clean (or vice versa)? A few examples would be gold.
* **Underlying Data & Freshness:** RF seems to pull from a wider, more proprietary set of sources (phishing kits, dark web). VirusTotal aggregates many AV and URL scanners. In practice, which feels more *reactive* to new threats and which feels more *stable*?
* **False Positives in Automation:** This is my biggest worry. If I write a Terraform module that automatically blocks egress to domains with a risk score > 80, what's the potential for breaking legitimate services? For example:
```hcl
module "domain_risk_check" {
source = "./modules/risk_checker"
domain = var.external_service_endpoint
# Would you trust this logic?
allowed = local.rf_risk_score < 50
}
```
* **Use Case Fit:** Is RF's score better for *proactive* blocking during provisioning, while VirusTotal might be more of a *reactive* check in a CI/CD pipeline? Or maybe they're complementary and should be used in tandem with some weighting logic?
I'd love to hear about any real-world workflows, not just theoretical comparisons. Have you built any tooling that queries both APIs and compares the results? Any gotchas with API latency or cost when scaling these checks?
The goal here is to move from "this score looks scary" to "we can reliably automate based on this with a known confidence level." Any insights or war stories would be hugely appreciated!