AI Regulation Blind Spots: Underestimated Risks in Internal Deployment and Training

Recently, multiple AI safety researchers and evaluation organizations have pointed out a severe blind spot in current AI governance and regulation: an excessive focus on a model's "public release" while ignoring the massive potential risks hidden in internal lab deployments and early training stages.

Regulatory Blind Spots in Internal Deployment

In a paper, @StephenLCasper notes that frontier models deployed internally within companies often have fewer restrictions and greater capabilities than products aimed at external users. If regulation focuses solely on the "public release" milestone, it misses systems used exclusively internally and kept out of the public eye. @JacquesThibs also believes that market-facing products are no longer the most powerful or important systems, and governance focus must shift to the deployment phase of internal lab models before they become products. Because these models are only exposed to employees, it is extremely difficult for the outside world to ascertain their capabilities, usage, and risk signals, significantly weakening governance capabilities.

Risk Assessment Should Shift Forward to the Training Phase

A report by evaluation agency METR further emphasizes that AI models can pose dangers well before their official public deployment, so relying solely on "pre-release testing" is insufficient to mitigate risks. @DavidSKrueger urges that third-party evaluations should not only occur post-launch but must be pushed forward into the training process itself. If a model is sufficiently intelligent and becomes misaligned, it could already inflict substantial harm during the training or internal evaluation phases.

2026-07-22 ~ 2026-07-22 · 6 related posts