Canonical contracts between heterogeneous systems.

This transformation engine defines canonical AI Security LLC contracts that normalize heterogeneous partner-native objects into a stable interoperability layer. By mapping vulnerability findings, attack traces, evidence artifacts, workforce results, and learning events into shared contracts such as Finding, Evidence, Attack Trace, Attack Path Result, and Workforce Result, the system isolates security and learning semantics from any single vendor transport, endpoint, or proprietary data model.

The result is a consistent translation boundary between product-native inputs and mapped partner outcomes. Vulnerability findings become qualified paths and retest results, evidence becomes readiness evidence, attack traces become structured attack-path outputs, and LMS completion events become auditable workforce signals. This canonical layer makes integrations more durable, reduces coupling to vendor-specific schemas, and preserves meaning across disparate systems while enabling downstream analytics, automation, and program reporting.