Advances in protein language models (pLMs) and their integration into closed-loop wet-lab experimental platforms is unlocking powerful new capabilities in protein design. This convergence, termed Intelligent Automated Biology (IAB), enables rapid, large-scale exploration of protein function, accelerating discovery in fields from medicine to synthetic biology. Yet when applied to pathogens, these same tools pose serious dual-use risks. IAB systems can efficiently optimize immune escape, viral fitness, and other dangerous traits, even in the absence of deep biological expertise. In this position paper, we argue that the AI community must take proactive steps to address this emerging AI safety and biosecurity challenge. We introduce a framework categorizing IAB capability levels to guide risk assessment, examine IAB's unique governance challenges, and offer concrete recommendations for pLM-specific safeguard research.