Quantization in cybersecurity and AI refers to the process of reducing the precision of numerical values used in computations, such as weights and activations in neural networks. By converting high-precision numbers (like 32-bit floating-point) into lower-precision formats (like 8-bit integers), quantization reduces the computational resources and memory required for processing. This makes AI models faster, more efficient, and suitable for deployment on devices with limited hardware capabilities, such as smartphones or IoT devices. While quantization can slightly reduce model accuracy, it is a crucial technique for optimizing performance and enabling secure, real-time inference in resource-constrained environments.