Assess whether a split between models is a review queue or noise (15b86c)

Situation

In a firm whose vendor MSA is silent on training rights, content-attribution tags missing on customer-facing copy is the evidence after a scorecard that rated 100% of outputs 'acceptable'. Content-attribution program lead has to pick A split between models is a review queue or Noise for this AI Governance Layer Lifecycle and Accountability close using content-attribution tags missing on customer-facing copy.

Decision

Content-attribution program lead in a firm whose vendor MSA is silent on training rights must choose A split between models is a review queue / Noise using content-attribution tags missing on customer-facing copy after a scorecard that rated 100% of outputs 'acceptable'.

Hypotheses to test

• A scorecard that rated 100% of outputs 'acceptable' is noise around an already-controlled Lifecycle and Accountability process in a firm whose vendor MSA is silent on training rights, given content-attribution tags missing on customer-facing copy.
• A scorecard that rated 100% of outputs 'acceptable' is the event in content-attribution tags missing on customer-facing copy that forces A split between models is a review queue for content-attribution program lead under AI Governance Layer.
• Content-attribution tags missing on customer-facing copy shows a one-file miss after a scorecard that rated 100% of outputs 'acceptable', not a Lifecycle and Accountability program failure.
• Content-attribution tags missing on customer-facing copy cannot decide a split between models yet after a scorecard that rated 100% of outputs 'acceptable'; hold is the only AI Governance Layer close a firm whose vendor MSA is silent on training rights can defend.

Analysis required

• Map the control-plane score in content-attribution tags missing on customer-facing copy to the policy gate content-attribution program lead can enforce.
• Name the override that would let a split between models proceed without a silent bypass.
• Test whether a scorecard that rated 100% of outputs 'acceptable' changed routing, logging, or human-in-the-loop on the live agent path.
• For this AI Governance Layer Lifecycle and Accountability file, read content-attribution tags missing on customer-facing copy against a scorecard that rated 100% of outputs 'acceptable' and write the one fact that would move a split between models for content-attribution program lead.