You've controlled access (Day 1) and network paths (Day 2). Now protect the data and models themselves through encryption. ML pipelines have assets everywhere needing protection — training data in S3, running disks/volumes, node-to-node communication, model artifacts in S3, external credentials. Today: encryption at rest, encryption in transit, KMS key management, Secrets management.
Encryption splits by data location.