Here is a question we could not answer well a year ago: what really happens with choosing between DNS delegation and CNAME onboarding? We can answer it now.
What we changed
We moved the decision from a single threshold to a continuous score, added an explanation to every decision and made every rule testable against historical traffic before it goes live.
The result is fewer late-night pages for our analysts and — more importantly — fewer real users challenged by mistake.
Latency budget
Our budget for the whole filtering pipeline is one millisecond at the 99th percentile. Anything that cannot be decided within that budget runs asynchronously and influences the next request from the same client, not the current one.
That constraint shapes everything: data structures, where state lives and which signals we are willing to compute inline.
Observability first
Every mitigation decision is logged with its reasons, and every log line can be traced to the rule and score components that produced it. When a customer asks why a request was challenged, the answer is a link, not a guess.
Logs stream to the customer’s SIEM within seconds, which also means their security team sees attacks in the same tools they use for everything else.
What we got wrong
Our first version challenged too eagerly on mobile networks, where thousands of real users share a handful of carrier-grade NAT addresses. Reputation that is shared is reputation that is noisy.
We now weight fingerprint consistency and session behaviour far more heavily than IP reputation for traffic from known mobile carrier ranges.
Why per-route baselines matter
A thousand requests per second to your homepage is Tuesday. A thousand requests per second to your password-reset endpoint is an attack. Global rate limits cannot tell the difference; per-route baselines can.
We learn the normal shape of traffic per route and per hour of the week, so a surge on a sensitive endpoint raises the risk score long before it approaches a global threshold.
Protecting the origin
None of this matters if the attacker can reach your origin directly. Historical DNS records, certificate transparency logs and misconfigured subdomains leak origin addresses all the time.
Allow-list the edge ranges, enable authenticated origin pulls and treat any origin IP that ever appeared in public DNS as burned.
What actually happens during a flood
The first thing to fail during a layer 7 flood is almost never bandwidth. It is connection slots, worker processes or database connections on the origin — resources measured in hundreds or thousands, not gigabits. An attacker who can make each request expensive only needs a few thousand requests per second.
That is why we score requests before they are proxied. By the time a request reaches your origin, it has been attributed to a client, compared against that client’s history and weighed against the current load on the route it targets.
{ "match": { "path": "/api/v1/checkout", "risk": ">= 50" }, "action": "challenge" }
As always, questions and corrections are welcome at [email protected].
The point about origin IPs leaking through certificate transparency logs is underrated.
Great write-up. We saw almost the same pattern on our login endpoint last month.
Thanks! Yes — the risk score and its components are included in every log record.
The point about origin IPs leaking through certificate transparency logs is underrated.
We had the exact false-positive issue with CGNAT carriers. The weighting change makes sense.
Great to hear, thanks for sharing your experience.
Could you share the dataset behind the percentages?