AI Red Teaming

Definition

AI Red Teaming is structured adversarial testing of AI systems to expose unsafe, biased, exploitable, or brittle behavior. In defense applications, it finds failures before an enemy, user, or environment does. The hard part is coverage gaps and tests that become obsolete as models change, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a continuous KhanBMS practice, not a one-time acceptance event, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

evaluation discipline

Operational value

Finds failures before an enemy, user, or environment does

Primary risk

Coverage gaps and tests that become obsolete as models change

KhanBMS role

A continuous KhanBMS practice, not a one-time acceptance event

Related terms

• Adversarial Machine Learning (AML)
  Study and defense of attacks that manipulate AI through crafted inputs, poisoned data, or model theft.
• Multi-Agent Debate (MAD)
  Technique where multiple model agents argue, critique, and revise answers before a decision is surfaced.
• Jailbreak Resistance
  Defenses that stop users or inputs from bypassing model safety and policy constraints.
• Autonomy Test and Evaluation (T&E)
  Test discipline for validating autonomous systems across simulation, hardware, field trials, and adversarial scenarios.