When Red Teaming meets AI
The easiest test to pass is the one that always runs the same way.Real attackers, however, do not work from a pre-prepared checklist. They observe, adapt, and look for connections between weaknesses that may seem harmless individually. That is why good Red Teaming should not simply repeat familiar scenarios.AI enables us to make testing more dynamic. We use it to process results from different tools faster, track more test hypotheses, and discover dependencies between individual findings.Imagine an environment with dozens of accessible services, different user roles, and multiple configuration gaps. A classic scan would sort them into a list. AI can help ask the more important question: which of them could be combined into a real attack path?This is not just a theoretical possibility. Research presented at USENIX Security 2024 shows that a specialized AI framework can successfully interpret results from security tools, connect findings from different stages, and suggest follow-up actions. The same research also clearly points out the limitations — loss of context, hallucinations, outdated information, and poor technical decisions. USENIX Security: PentestTherefore, the AI does not take control of the test. It operates within a predefined scope, and each hypothesis and action is checked by a Red Team specialist.It is in this combination that the real value lies: AI can process more options, but the expert understands which ones matter. AI can suggest a route, but the specialist assesses whether it is realistic, safe and relevant to the business.At Cybernetics, we integrate AI where it improves test quality — without replacing expertise and without turning security into an uncontrolled experiment.Because the goal is not to execute more automated attacks.The goal is to discover that scenario for which the defense is not yet prepared.
