Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance

A recent research paper highlights a critical gap in current AI governance, which predominantly focuses on the training phase of AI models. The analysis suggests that AI capabilities are increasingly evolving during the deployment (inference) stage through various scaling and scaffolding methods. The paper proposes a feasibility taxonomy of twenty inference-time governance mechanisms, categorized by monitoring, verification, and enforcement, assessing their readiness based on a four-vendor evidence base.

This research identifies a growing vulnerability in AI governance frameworks, as critical capabilities are emerging beyond the scope of current regulatory focus. Addressing inference-time governance is essential for comprehensive risk management and ensuring the responsible development and deployment of advanced AI systems across various sectors.

Current AI governance primarily targets the training phase of AI models.

Forward consideration, not a verified fact.