Quantum Key Distribution (QKD) provides a provably secure method for key exchange based on quantum mechanics. However, the deployment of QKD in Internet-ofThings (IoT) environments remains challenging due to the resource constraints of typical edge devices. In this work, we explore the use of the Guessing Random Additive Noise Decoding (GRAND) algorithm as a lightweight alternative to conventional error correction techniques in QKD postprocessing. GRAND decodes by directly guessing noise patterns, eliminating the need for iterative belief propagation or large matrix storage. We evaluate soft-output (SO) GRAND on short block-length linear codes under varying channel conditions. Results show that SOGRAND achieves lower frame error rates (FER) and average decoding times than LDPC in typical QKD scenarios, demonstrating its potential for resource-constrained quantum-secure IoT systems.