A learning attacker that hardens an adaptive defender.
A learning red-team continuously attacks the decision core to find weaknesses before a real adversary does. It is strictly walled off from decisions.
Always attacking
The red-team continuously probes the live defender for weaknesses a human team would take months to find.
Walled off from decisions
The red-team never touches the decision path. The defender stays reproducible and explainable, and the red-team only attacks it.
Every weakness closed
Each weakness it finds is fixed as an auditable change, then re-verified.
It has already earned its keep.
The red-team has found two real structural weaknesses in the decision core, and we fixed both, each as an auditable change, then re-verified.
That is the loop working as designed. A learning attacker makes the defender stronger, without ever becoming part of it.
Why the red-team never decides
If the red-team could steer the defender, an adversary could try to steer it too. So it only attacks: its job is to break our assumptions before a real adversary does.
Ask us what the red-team found.
The two structural findings, and how each was closed, are covered in the technical briefing.