AI Risk Management Framework/ AI RMF

Definition

AI Risk Management Framework is structured approach to identifying, measuring, managing, and governing AI risks across the lifecycle. In defense applications, it turns abstract AI safety concerns into repeatable controls and reviews. The hard part is checkbox compliance and poor mapping to battlefield tempo, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a governance layer KhanBMS can map to mission authority and assurance controls, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

governance framework

Operational value

Turns abstract AI safety concerns into repeatable controls and reviews

Primary risk

Checkbox compliance and poor mapping to battlefield tempo

KhanBMS role

A governance layer KhanBMS can map to mission authority and assurance controls

Related terms

• Responsible AI for Defense (RAI)
  Governance practices that align military AI with lawful, ethical, reliable, and accountable use.
• Autonomy Test and Evaluation (T&E)
  Test discipline for validating autonomous systems across simulation, hardware, field trials, and adversarial scenarios.
• AI Red Teaming
  Structured adversarial testing of AI systems to expose unsafe, biased, exploitable, or brittle behavior.
• Model Cards for Defense
  Documentation artifacts describing model purpose, training data, metrics, limits, and approved uses.