Safety controls for Indic language generation must account for multilingual variation and culturally grounded harm categories that are underrepresented in English-centric resources. We present IndicSteer, an initial study of inference-time activation steering for safety across 8 harm categories and 9 Indic language settings, based on contrastive directions computed from safe/unsafe response pairs. To the best of our knowledge, this is the first application of Contrastive Activation Addition (CAA) to Indic LLMs. Evaluation uses a structured LLM-as-a-judge protocol with strict isolation by category and alpha, covering ≈12,960 prompt-response pairs. We report harmful-response and coherence metrics for Sarvam-1 and OpenHathi (Hindi track), and present cross-lingual representation structure via linear CKA for Sarvam-1 and Krutrim-2-Instruct. On matched slices, Sarvam-1 at 𝛼=12 reduces harmful rate from 73.47% to 41.34% (32.13 pp; 43.73% relative) with no additional retraining. For OpenHathi Hindi, harmful rate falls monotonically from 85.83% (baseline) to 27.13% at 𝛼=15, a 58.71 pp total reduction.