AI Supply Chain Security

Definition

AI Supply Chain Security is protection of datasets, weights, code, dependencies, tooling, and deployment pipelines for AI systems. In defense applications, it treats the AI lifecycle as part of the operational attack surface. The hard part is compromised packages, poisoned datasets, leaked weights, and build-system tampering, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a prerequisite for trusting KhanBMS fielded autonomy, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

security discipline

Operational value

Treats the AI lifecycle as part of the operational attack surface

Primary risk

Compromised packages, poisoned datasets, leaked weights, and build-system tampering

KhanBMS role

A prerequisite for trusting KhanBMS fielded autonomy

Related terms

• Secure Model Provenance
  Cryptographic and procedural evidence tracking where a model, adapter, dataset, or artifact came from.
• AI Bill of Materials (AIBOM)
  Inventory of models, datasets, adapters, tools, dependencies, licenses, and provenance in an AI system.
• Data Poisoning
  Attack that corrupts training or fine-tuning data to implant bad behavior or degrade performance.
• MLOps for Defense (MLOps-D)
  Lifecycle practices for building, testing, approving, deploying, monitoring, and updating military AI.