Large reasoning models (LRMs) are strong multi-step problem solvers, yet their long reasoning traces may gradually drift into unsafe compliance when exposed to harmful or adversarial prompts. Existing safety alignment methods can reduce harmful outputs; however, they often rely on broad refusal or supervision over full safe reasoning traces, which may weaken the reasoning behavior that makes LRMs useful. We introduce Intent–Risk–Decision (IRD), a lightweight prefix-tuning method that steers only the beginning of the reasoning trace: the model first emits a prompt-intent tag, a risk statement, and a compliance decision, and then continues under its original reasoning policy. Across three LRMs and evaluations covering harmfulness, jailbreak robustness, over-refusal, and reasoning ability, IRD substantially reduces unsafe outputs while preserving reasoning performance. In particular, IRD lowers average harmfulness to 1.2–7.4% across the evaluated backbones and keeps near-lossless reasoning performance, %within 0.2 average points of the base models, showing that targeted early steering can improve safety without supervising complete safe traces or full model fine-tuning. Code and data are available at https://github.com/ReXT9527/IRD.