AML - Khan BMS Battlefield Management System
A short, opinionated brief on AML — Adversarial Machine Learning — and the role it plays inside a Khan BMS formation under contested conditions.
Strip the marketing and AML is exactly what the standard says: Adversarial Machine Learning. Study and defense of attacks that manipulate AI through crafted inputs, poisoned data, or model theft. Adversarial Machine Learning is study and defense of attacks that manipulate AI through crafted inputs, poisoned data, or model theft. In defense applications, it prepares defense systems for enemies who attack the model, not only the platform. The hard part is adaptive attacks and incomplete test coverage, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a standing threat model for every KhanBMS AI module, tying the concept back to modular command, edge execution, and auditable authority.
Inside Khan BMS, AML is exposed to mission planners as a capability bundle rather than a vendor SDK. The planner composes effects out of AML-derived primitives; the integration path for new hardware is a manifest, not a code branch.
The Zuun (one hundred nodes) is the natural composition point for AML. Ten Arbans aggregate their AML state into one Zuun-level picture; one Zuun commander supervises ten subordinates, never a hundred individual feeds. The cognitive-load math is the entire point.
AML is a cost-curve question disguised as a technical one. If the per-node integration cost does not collapse, the standard does not matter.
If AML matters to your formation, the integration question is not whether to support it. It is how cleanly the rest of your stack survives when it is the only thing still working.