Keeping our control plane alive when the data plane is on fire (part 2)

keeping our control plane alive when the data plane is on fire sounds like a narrow topic. It turns out to touch almost every part of how an edge network behaves under attack.

Compliance is a side effect

Regulators increasingly ask for evidence of resilience, not just promises. An incident timeline with start, peak, vectors and impact is exactly the evidence DORA and NIS2 ask for — and it falls out of good observability for free.

We export incident reports in formats auditors can file without anyone rewriting them.

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.

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.

Challenges beat blocks

Blocking lists go stale within minutes when attackers rotate through residential proxies. Proof-of-work does not care where a request comes from; it only cares whether the client is willing to pay the cost.

For a real browser that cost is a few hundred milliseconds, once per session. For a botnet sending a million requests a minute it is a million puzzles a minute — and at that point the attack stops being cheap.

The economics behind it

A booter service rents out a 100 Gbps attack for less than the price of a pizza. Defending against it with bandwidth alone is a race you lose. Defending against it by making each malicious request cost more than it earns is a race you win.

This is the entire idea behind never metering attack traffic: our costs scale with filtering, not with your invoice.

Measuring success

We track three numbers for every incident: time to mitigation, the share of attack traffic that reached the origin, and the share of legitimate traffic that was challenged. The first should be under a second, the second under 0.1% and the third under 1%.

Those numbers go into every incident report, and they are the same numbers we are measured against in our SLA.

Testing in production, safely

Every rule starts in log mode. We replay the previous seven days of traffic through it and show exactly which requests it would have affected before anyone can promote it to challenge or block.

This one feature has prevented more incidents than any detection we have ever shipped.

{ "match": { "path": "/search", "risk": ">= 32" }, "action": "challenge" }

We will follow up with the numbers from the next quarter.