DeepSeek employs multiple layers of encryption to protect data during model training, focusing on securing data at rest, in transit, and during processing. For data at rest, such as datasets stored in databases or cloud storage, DeepSeek uses industry-standard encryption protocols like AES-256. This ensures that raw data, preprocessed datasets, and training checkpoints remain encrypted on disks or storage systems. For example, if training data is stored in AWS S3, server-side encryption with AWS Key Management Service (KMS) might be applied to automatically encrypt files before they’re written to disk. Similarly, data backups or snapshots are encrypted to prevent unauthorized access even if physical storage media is compromised.
During data transmission, DeepSeek secures communication channels using TLS 1.2 or higher to encrypt data in transit. This applies to scenarios like transferring raw data from user endpoints to training clusters, moving data between microservices in a distributed system, or syncing model updates across nodes in federated learning setups. For instance, when a client uploads a dataset via an API, TLS ensures the payload is encrypted end-to-end. Internally, service-to-service communication within training pipelines (e.g., between data loaders and preprocessing modules) might use mutual TLS (mTLS) for additional authentication and encryption. Network-level security measures like VPNs or private subnets in cloud environments further isolate training infrastructure from public access.
For data actively being processed during training, DeepSeek combines encryption with strict access controls and hardware-based security. Training jobs often run in isolated virtual private clouds (VPCs) with firewalls restricting inbound/outbound traffic. Temporary decryption of data in memory is tightly managed—keys might be stored in hardware security modules (HSMs) or cloud-based KMS systems, with automatic rotation policies. For example, a GPU cluster training a model could fetch decryption keys from an HSM only during the initialization phase, ensuring keys aren’t exposed in logs or runtime memory. Additionally, frameworks like TensorFlow Privacy or PyTorch’s cryptographic toolkits might be integrated to apply techniques like differential privacy or encrypted computation for sensitive operations, adding another layer of protection during gradient updates or inference. Post-training, data remnants in memory or temporary storage are securely wiped using tools like shred or cloud provider-specific data destruction APIs.