In today's cloud, to make your data searchable, you give up its contents to your cloud provider, even if they then encrypt it. While you gain the speed and power of the cloud, you do so by sacrificing the privacy of your data, a common barrier to cloud adoption. Hence, to encourage the migration of sensitive data from behind the firewall to the cloud, we need to process that data without ever decrypting it. We'll demonstrate the state of the art of processing encrypted data using GPU-accelerated cloud. We'll also present a roadmap for near-future plans for cryptographic schemes for secure transcription. Inspired by fully homomorphically encrypted convolution nets for secure image processing, so-called CryptoNets, we'll demonstrate a CNN-based acoustic model and discuss in broader terms how the CryptoNet idea extends to other types of deep learning network, such as RNNs.