Unlike most areas of cybersecurity, a DDoS attack succeeds for one reason: a vulnerability in the DDoS protection deployed to block it. The application is not the weak point. The defense is. A specific combination of attack vector, target, service, and protection layer reaches the target, the defense fails to block it automatically, and the organization’s online services go down.
This problem predates AI, but AI makes it more urgent.
Attackers can now use LLMs to find and exploit those gaps faster than a human ever could, and to produce attack patterns your defenses have never seen.
The question for a security leader is simple: If an attacker generates a new DDoS attack tomorrow, do you know whether your current configuration will block it?
For most organizations, that data does not exist. You cannot fix what you have not tested, and you cannot test for an attack you have not seen. RADAR VectorAI™ from MazeBolt closes that gap.
AI-Orchestrated vs. AI-Generated: Why the Difference Determines How You Defend
Not all AI DDoS attacks are the same, and the distinction matters for how you defend against them.
The first is AI-orchestrated. A model selects from known attack vectors and sequences them to probe for the path that still gets through deployed defenses. The individual vectors are familiar. The intelligence is in the selection and the timing.
The second is AI-generated. A model produces novel traffic patterns, attacks your defenses have never encountered and your team has not seen before. These are the vectors that bypass protections tuned only against signature-based attack vectors.
Defending against the first requires broad, continuous coverage. Defending against the second requires something no legacy pen testing service can provide. First, it requires crafting novel, never-been-seen DDoS attacks. Then, RADAR can deliver these attacks safely against your live environment, so you can measure the outcome.
Generate the Attack. Deliver It. Measure the Result.
VectorAI uses AI to generate large-scale, randomized, never-before-seen DDoS attack vectors, tailored to each specific environment. These are not replays of past attacks. They are new vectors built to match the speed and scale of what an AI-equipped attacker can now produce.
RADAR then delivers those vectors the way it delivers signature-based attacks: continuously, nondisruptively, against the live production environment. The attack runs against the real configuration, across every target and every protection layer, from the CDN to the scrubbing center to the CPE to the WAF, across OSI Layers 3, 4, and 7.
For each AI-generated vector, RADAR shows whether the defense mitigated it automatically, partially mitigated it, or let it through, and which layer failed. This is unique data that tells you exactly where the vulnerability sits and where to tune the configuration before a real attacker finds it.
This data has a second use. You can feed the vulnerability data back into your DDoS defenses to train them to block AI-generated attacks automatically. So you do two things at once: find the gap, and produce the specific data your DDoS protection needs to close it and keep it protected as your environment changes.
The Testing Model that AI Has Already Outpaced
A point-in-time DDoS pen test runs once or twice a year, requires downtime, and covers a small slice of the attack surface. Against AI-generated attacks, that model breaks down completely.
Configurations drift. Services change. Attackers generate new vectors faster than any annual test can account for. If you test once a year, you do not know what your defenses look like the rest of the year, and you certainly have not tested them against attacks that emerged after your last assessment.
MazeBolt research shows how wide this gap already is. In a survey of 300 CISOs and security leaders across BFSI in the US and Europe, conducted with Global Surveyz, 86% test their DDoS defenses once a year or less. Just 5% said that they are confident their DDoS investments actually stop attacks. Moreover, only 9% were familiar with continuous, nondisruptive testing, yet 97% saw value in it once they understood it. None described their defenses as fully automated. Moreover, the organizations surveyed suffered an average of 3.85 damaging DDoS incidents in the prior year, despite 85% having increased their DDoS budgets.
More investment did not close the gap, because spending is not the measure. The key question is whether services stay online when an attacker finds the one path that still gets through.
The Goal: Automated Mitigation that Needs No Human Intervention
The goal is not more testing for its own sake. The goal is higher automated mitigation, so the defense blocks an attack without manual intervention. Manual response (triggering an SLA) means the damage has already begun.
MazeBolt has found that enterprises are, on average, 37% vulnerable when they first deploy RADAR, and that figure typically drops to around 2% after remediation. Applied to AI-driven attacks, the same principle holds. The way you raise automated protection from roughly 63% to above 99% is by means of real vulnerability data: data generated against your live environment, covering known, AI-orchestrated, and AI-generated attacks.
VectorAI crafts the AI-generated attacks. RADAR delivers them and measures the result. Together, they give you the one thing that has always been missing from DDoS defense: proof, in practice and not in theory, that your protection blocks what an AI-driven attacker can now throw at it.
Protection without validation is not enough.
To learn more about RADAR VectorAI, speak with an expert.
Key Takeaways about VectorAI
- VectorAI is a RADAR™ add-on module that uses AI to generate novel, never-before-seen DDoS attack vectors tailored to each environment.
- Those AI-generated attacks are delivered by RADAR continuously and nondisruptively, against the live production environment, with zero downtime.
- For each vector, RADAR shows whether the defense blocked it, or let it through, and which protection layer failed.
- The vulnerability data VectorAI produces can train DDoS defenses to automatically block AI-generated attacks.
- MazeBolt validates existing DDoS defenses. It does not replace them.
