Modern bot threats are increasingly driven by determined, sophisticated attackers. Often it is not even one person, but a group trading techniques with each other or a commercial service sold to anyone willing to pay. For many of them, getting past bot detection is a full-time job they genuinely enjoy.
Block them and they get to work, finding a workaround. AI has simplified this further, making it even easier to set up complex configurations for attackers, lowering the overhead of an attack. This shift puts defenders at an economic disadvantage.
Responding and adapting to new attacks takes care, evidence, and effort to ensure efforts to block attackers don’t impact real users on the way. Attackers have no such concerns and are primarily constrained by their time and their pool of proxies, and ensuring their infrastructure providers don’t shut down their accounts. Their advantage is the cost of adaptation.
Attackers can adapt as often and continuously as they need, while most defenses are deployed in discrete, managed releases. Cloudflare analyzes more than a trillion requests a day for signs of automated abuse, so we see how fast attackers change tactics. That gap in responsiveness is widening.
The inconvenient truth: bot detection across the industry often rests on a hopeful assumption that if you make the wall tall enough, attackers stay out. In reality, a determined attack always finds a way through. The question is not whether a determined attacker can get through.
They will. The question is what happens when they do. Today we are launching Adaptive Intelligence, a new bot detection engine that starts from the opposite idea.
Rather than betting on a wall that keeps every attacker out, Adaptive Intelligence makes getting through so slow and costly that the attack stops being worth running. We believe that no other bot detection works this way. One attacker, many disguises Not every attack is obvious to spot.
The most sophisticated ones are built to disappear into ordinary traffic. An attacker can spread requests across a large residential proxy network, keep the rate from each address low, and move patiently through a login, checkout, or account-recovery flow. Every request comes from a different address, often with a fresh user agent or a new bot fingerprint, so each one looks like a new visitor.
No single source ever crosses a rate limit. This is what makes the shape so hard to stop. Tighten the thresholds too far and real customers are turned away, which is the outcome you are trying hardest to avoid.
The attack lives in the space between one request and the next, and a defense that studies each request on its own will never see it. The flaw of deterministic detection The challenge with rule-based systems is that they hand the attacker a stationary target. They iterate in days while the model waits months for its next update, so by the time it catches up, the tooling has already moved on.
Bot detection has always answered a new attack technique by writing a rule to catch it. That works, until the attacker studies the signal, learns how to circumvent it, and forces another rule to be written. Some of the most advanced attackers have even created tooling to semi-automate this process.
The defender appears permanently disadvantaged. This kind of detection is “deterministic”, meaning that the same input always produces the same output. A defense that never changes teaches the attacker how to beat it and indirectly drives bot operators to build more capable automated attacks.
Against a deterministic defense, automated probes return a clean yes or no, and over enough attempts that feedback teaches an attacker exactly where the edges of the system are. The economics are in the attacker’s favor. Changing the economics of attack Adaptive Intelligence aims to reverse the economics and put them back in the defender's favor.
A defense that keeps changing flips that calculation, but only if two things are true at once. First, it has to cost the defender less to react than it costs the attacker to work around it. Second, attackers must be starved of the feedback they use to adapt, so they cannot simply learn their way back in.
Get both right and the attacker's own loop turns against them: nothing they learned stays true, and each new attempt costs more than the last, until the attack is no longer worth running. Part of that is giving an attacker less to learn from. Adaptive Intelligence can recognize a bot from a signal without visibly reacting to it, so the attacker keeps relying on a tell they do not realize we can see.
And it treats detection as a statistical judgment rather than a fixed rule. That makes it non-deterministic. It weighs many signals at once, so there is no single piece of logic for an attacker to isolate and beat.
A new detection engine Your bot score already comes from several detection methods working together: machine learning, behavioral validation, JavaScript fingerprinting, a library of heuristics, and checks that recognize known, verified bots like search crawlers. Adaptive Intelligence is a brand new bot detection engine that sits behind bot score. Where every other system is built to keep attackers out by accumulating rules, Adaptive Intelligence is built with the assumption that attackers will eventually get in, and makes that attempt as costly as possible.
Below, we explain three components our Adaptive Intelligence detection engine will have, that are unique when compared to traditional models: improving itself, disposable rule generation, and learning from the traffic it protects. Launching today is its first component: the machine learning at the center of your bot score, now retraining continuously instead of shipping as a fixed version.
It aggregates network signals from across Cloudflare's network and measures the probability of automated abuse for every request. Where a fixed model sits still, Adaptive Intelligence keeps moving.
Originally published at blog.cloudflare.com
