Source of record
Where this definition comes from
Anthropic Risk Report, August 2026, §1
“We consider all of our models, including those we run only internally, in our assessment.”
https://www-cdn.anthropic.com/f61d49fa5596956a5dec75fea0e973bf6a6a8378/Redacted%20Risk%20Report%20August%202026%20.pdfGoogle DeepMind Frontier Safety Framework v3.1, glossary
“Internal Deployments: represent model releases restricted to Google employees for internal use.”
https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/strengthening-our-frontier-safety-framework/frontier-safety-framework_3-1.pdf
Crosswalk
How named organisations use this concept
Every row below is a shadow mapping. It is CASRAI's own reading of a published document. No lab, evaluator or regulator named here has declared, endorsed, or been consulted on this mapping. That will change only when an organisation files its own Mapping Declaration — see the non-endorsement policy.
| Organisation | Their term, as published | Match | Source |
|---|---|---|---|
| Anthropic Anthropic Risk Report, August 2026 | “"We consider all of our models, including those we run only internally, in our assessment." "Note that for most models, including all early snapshots of models intended for eventual broad public release, we do not have strict technical safeguards on internal deployment"” | exact confidence: high | Anthropic Risk Report, August 2026 |
| OpenAI Preparedness Framework v2 / Pacing Model Development (Cyber), Aug 2026 | “PF covered deployments include significant internal agentic systems; "Misalignment safeguards meeting the High standard (C.2) for large-scale internal deployment"; "Highest-risk workloads: 'internal deployments of frontier models and frontier RL training runs'"” | close confidence: medium | OpenAI Preparedness Framework v2 |
| Google DeepMind Frontier Safety Framework v3.1 | “"Internal Deployments: represent model releases restricted to Google employees for internal use." "High-Risk Internal Deployments: ... for use cases with the potential to enable severe threat scenarios (e.g. building internal security infrastructure or automating ML R&D)."” | exact confidence: high | Google DeepMind Frontier Safety Framework v3.1 |
| Meta Meta Advanced AI Scaling Framework v2 | “"Internal deployment: models that are exclusively available to Meta personnel"; "internal-use risk report" provided "as appropriate" to "relevant authorities"; "Loss of Control risks may occur with similar probability with any type of deployment, including internal deployment."” | exact confidence: high | Meta Advanced AI Scaling Framework v2 |
| EU EU GPAI Code of Practice, Safety and Security Chapter / OpenAI Frontier Governance Framework | “The chapter applies across "the entire model lifecycle (including during development that occurs before and after a model has been placed on the market)" rather than singling out "internal deployment" as a separate category; Measure 3.2's model evaluations and Commitment 4's acceptance determination must be completed "at least before placing the model on the market" (Measure 1.2), leaving pre-market internal use inside the same process rather than exempted from it” Narrower coverage than OpenAI's FGF citation of this same chapter for internal-use oversight-circumvention risks specifically. | close confidence: medium | EU GPAI Code of Practice, Safety and Security Chapter |
| California SB 53 California SB 53 | “Framework topic "(10) Assessing and managing catastrophic risk resulting from the internal use of its frontier models"; summaries to OES "every three months or pursuant to another reasonable schedule"; incident field "(4) Whether the incident was associated with internal use of a frontier model." "Internal use" is undefined.” SB 53 requires reporting on internal use but leaves the term "internal use" itself undefined — recorded, not silently dropped. | exact confidence: high | California SB 53 |
| METR METR (metr.org) | “Frontier Risk Report participants provided "Access to their most capable internal model(s) at the time of assessment, including raw chains of thought"” | close confidence: medium | METR |
