How do you design encryption and key management for an ML platform on AWS?

Updated Aug 1, 2026

Short answer

Encrypt data at rest in S3, EBS volumes and Feature Store with customer-managed KMS keys, enable inter-container encryption for distributed training, scope key policies per data domain, and remember that KMS permissions are separate from S3 permissions.

Deep explanation

Encryption in an ML platform spans more surfaces than people expect, and each has its own parameter.

At rest

S3 — SSE-KMS with a customer-managed key (CMK). SSE-S3 is simpler but gives no key policy, no per-domain separation and no CloudTrail record of key use.
Training/processing volumes — volume_kms_key encrypts the attached EBS volume, which holds your data while the job runs.
Model artefacts — output_kms_key encrypts what training writes.
Feature Store — separate keys for online and offline stores.
Studio EFS — the domain's home directories.…

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