Our initial implementation of geographic threat mitigation on Radware's Cloud WAF created an immediate and severe operational incident: we successfully blocked a significant volume of malicious traffic from regions we had no business in, but we also severed all connections from our legitimate, contracted partners in South Korea and Japan. The error was architectural, not merely configurational. We treated geo-blocking as a simple "allow/deny list" at the edge, which is fundamentally incompatible with a globally distributed business presence.
The core failure was applying a blanket country-code block at the WAF policy level, which lacks the granularity to distinguish between, for example, a malicious script kiddie in Country X and a known-good API client from a partner's data center in that same country. The solution required a layered security model that decouples *initial filtering* from *trusted source validation*. Here is the refined workflow we deployed:
1. **Initial Geo-Filter at the Edge:** A broad, restrictive geo-block is applied at the Radware WAF policy level. This catches the bulk of unsophisticated, geographically-based attacks.
2. **Exception via Verified Source Lists:** Legitimate traffic from blocked regions is permitted only if it passes a subsequent, more granular check. This is achieved by leveraging Radware's "Source IP/CIDR" allow list feature, which is evaluated *after* the geo-policy but before the request is passed upstream.
The critical implementation detail is the orchestration of the allow list. Maintaining a static list is untenable. We manage it as dynamic infrastructure, treating the allow list as code. Our partners' egress IPs are published as AWS Security Group IDs, which we then ingest and transform into a Radware-compatible format using a Terraform module.
```hcl
# Example Terraform data source to fetch partner egress IPs (conceptual)
data "aws_security_group" "partner_asia_egress" {
id = "sg-0xyz123"
}
# Module to convert and update Radware Source List
module "radware_asia_allowlist" {
source = "./modules/radware-ip-list"
list_name = "partner-asia-legacy-egress"
ip_cidrs = data.aws_security_group.partner_asia_egress.ipv4_cidr_blocks
waf_policy_id = radware_waf_policy.main.id
}
```
This setup is coupled with a CI/CD pipeline that validates changes and applies them on a scheduled basis. The observability pillar is covered by shipping Radware WAF logs to our SIEM, with a dedicated dashboard tracking:
* Total requests blocked by geo-policy.
* Requests from blocked regions that were permitted via source list.
* Alerting on any successful request from a blocked region *not* on the source list (potential false negative or new partner infrastructure).
The result is a defensive posture that is both aggressive and precise. We maintain a default-deny stance for entire geographic regions, while programmatically allowing a continuously verified set of legitimate business IPs. This pattern has proven robust and is now a template for other nuanced access-control scenarios, like permitting specific security scanner ranges while blocking general vulnerability scan traffic.
--from the trenches
infrastructure is code
Oh man, that's such a classic, and painful, lesson. Treating any security control as a simple binary switch is almost always a path to trouble. Your point about the architectural failure really resonates. I've seen similar pain with IP allow-listing when a critical SaaS provider decides to roll out a new AWS region overnight and suddenly whole workflows break.
I think your layered model is the only sane approach. That initial broad filter is your cheap, noisy gatekeeper. The real magic, as you're hinting, is in the verified source list. Are you managing that exception list dynamically, maybe via an API call from your internal directory of partner IPs into the WAF, or is it a more manual curated list you update? The automation potential there is huge, letting you keep the initial block tight while trust is managed elsewhere.
hugo
Totally. That automation piece is key, and where we almost got caught again. We started with a manually updated list, but partner IPs, especially for SaaS providers, can shift faster than our change control.
We ended up building a small orchestrator (in Make, but Zapier would work) that watches our partner directory's API for IP range updates. It then pushes those, with a partner-specific tag, to the WAF via its API. The WAF rule just checks for that tag on the incoming IP. No manual list edits, no lag.
The caveat? You have to trust that source directory's API implicitly. We had to add some serious validation logic before the webhook hits the WAF, because bad data from the source would instantly become a global allow. It's a new single point of failure, but manageable.
Webhooks or bust.
Yeah, that "architectural, not configurational" point really hits home. We ran into the same blind spot when we set up a similar geo-fence, but ours was on a CDN level. The moment we flipped the switch, our entire APAC sales team's demo environment went dark because their VPN provider routed through a blocked country. Oops!
Your layered model is the way. The big shift for us was mentally separating "this traffic is from a risky place" from "this traffic is *known and approved*" as two totally different security checks. It's not one rule with exceptions, it's two rules in a specific order. Saves so much headache.
Love that you're building out the exception list. Have you considered adding a secondary verification layer, like a required API key or client certificate, even for those trusted IPs? It adds a bit of overhead but closes the loop if a partner's IP range ever gets compromised.
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