Researchers have introduced PALACE, a novel adaptive-landmark kernel for certified point-cloud and graph classification, building upon the existing PLACE framework. This new approach incorporates a cover-theoretic core, utilizing the Lebesgue-number criterion on landmark covers to provide four closed-form guarantees, including a structural lower distortion bound. The PALACE framework allows for data-adaptive classification with a small cross-validation tier, enabling efficient tuning of key parameters such as budget, radii, and bandwidth. The implications of this work extend beyond the realm of machine learning, as state-aligned threat activity can raise the stakes from criminal to geopolitical, affecting not only the immediate target but also the broader landscape. This development matters to practitioners because it enables more robust and efficient classification of complex data, which can be critical in detecting and mitigating potential threats1.