What it is
A deployment without an owner has no one who can say no. A conformity gate names the person with that authority and makes their sign-off a condition of going live. The framework cited below grades the authority to deploy by measured zones of risk, from routine use with monitoring to suspension.
What it pushes on in the Lab
In the Lab, this lever engages deployment authority, whether someone with real power to change or halt the deployment is involved in it. At its strong tier it also puts a ceiling on the failure regime, because the gate holds back the riskiest operations.
You can also pull this lever at a strong tier, which costs more. At the strong tier, each effect below the Lab's strongest setting pushes harder.
The strong tier also pushes on these:
The Lab applies this lever to the whole network. It acts only on gauges that read the whole deployment, so it has no single part to aim at.
Its pattern in the Practice Library
The Practice Library describes the pattern behind this lever:Conformity assessment gate
The pressures it answers
No pressure page lists this lever among the levers that answer it.
Where you can pull it
Networks in the Lab that offer this lever:32
- Allegheny Hello Baby
- Arkansas ARChoices / ARIA
- BOSCO (Spain)
- CHAI (chronic-homelessness prediction)
- Eckerd Rapid Safety Feedback
- Forsakringskassan VAB fraud-selection profile (Sweden)
- Gladsaxe model
- Hackney / Xantura Early Help Profiling
Every network that offers it
- Illinois Rapid Safety Feedback
- Indiana / IBM eligibility modernization
- Insight Bristol / Think Family Database
- Kaiser Permanente Suicide-Risk Model
- Limbic Access (NHS Talking Therapies)
- Los Angeles County Project AURA
- Michigan MiDAS
- NarxCare
- Nevada DETR generative-AI unemployment appeals
- New Zealand MSD Predictive Risk Modelling
- NYC MyCity business chatbot
- Oregon Safety at Screening
- ProKid (Netherlands)
- REACH VET
- Samagra Vedika
- Serbia Social Card (Socijalna karta)
- Sistema Alerta Niñez (Chile)
- SyRI (Netherlands)
- Tennessee TennCare TEDS
- The same AI under full guardrails: the professional office
- VI-SPDAT
- What Works for Children's Social Care ML pilots
- Woebot (a governed app wind-down)
- Xantura OneView (predictive homelessness flagging)
The evidence behind its effects
The Lab cites these claims from the evidence registry for this lever's effects.
A frontier risk-management framework in practice ties deployment authority to measured capability-vs-safety zones — green (routine plus monitoring), yellow (controlled with strengthened mitigations), and red (suspend).[3]
Shanghai Artificial Intelligence Laboratory. (2025). Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2507.16534
doi.org/10.48550/arXiv.2507.16534
Appears in: PAN framework development
Topics: ai-governance, ai-safety
Greenblatt, R., Denison, C., Wright, B., et al. (2024). Alignment Faking in Large Language Models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2412.14093
doi.org/10.48550/arXiv.2412.14093
Appears in: Evidence reverification (2026)
Topics: ai-alignment, ai-safety
Meinke, A., Schoen, B., Scheurer, J., Balesni, M., Shah, R., & Hobbhahn, M. (2024). Frontier Models are Capable of In-context Scheming [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2412.04984
doi.org/10.48550/arXiv.2412.04984
Appears in: Evidence reverification (2026)
Topics: ai-safety