KD-006 · Governance stack

AI Governance Stack

Make the operating controls required for governed AI assistance visible before a recommendation affects work.

Evidence level: Verified
Executive audience: CIO, COO, product leader
Publication status: Published after human review

Inspect the operating model

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AI Governance Stack. Make the operating controls required for governed AI assistance visible before a recommendation affects work. Business problem: AI assistance becomes unsafe or untrusted when source authority, review, and observability are missing from the workflow. Claim boundary: Code-backed RachelOS implementation pattern. It does not establish revenue, adoption, conversion, healthcare deployment, or a result outside the demonstrated implementation.

flowchart TB
  S[Source-aware facts] --> R[Bounded recommendation]
  R --> A[Human approval]
  A --> X[Governed action]
  X --> O[Outcome and audit log]
  O --> V[Operational visibility]

Why this matters

AI assistance becomes unsafe or untrusted when source authority, review, and observability are missing from the workflow.

Executive decision

Require source-aware facts, recommendation boundaries, human approval, and action logging as separate controls.

Claim boundary

Code-backed RachelOS implementation pattern. It does not establish revenue, adoption, conversion, healthcare deployment, or a result outside the demonstrated implementation.

Evidence summary

rachelos:ev-rachelos-human-approved-ai

AI drafts and recommendations wait in a dedicated operator queue for human review before anything is sent.

Boundary: Enforced in code, not promised. No send-rate metric.

rachelos:ev-rachelos-relationship-memory

Knowledge that lived in one person's head became a persistent, timeline-based per-relationship snapshot that survives outside any individual.

Boundary: Code-backed memory layer. No outcome metric implied.