Encryption key rotation using dataset size and predicted threshold time based on data ingest rates

A computer-implemented method of rotating encryption keys in a data storage system, comprising:

• training a machine learning (ML) model based on historical data of data ingestion rates in the data storage system;
• predicting a time to reach a threshold amount of encrypted data using the trained model;
• and
• determining whether or the predicted time has passed, and if so, performing a key rotation operation to use a new key to encrypt incoming future data.

A method of rotating encryption keys encrypting respective chunks of data in a data storage system comprising:

• implementing a size-based key rotation process rotating keys in accordance with maximum encrypted dataset set size as a threshold value;
• automatically triggering the key rotation process based on a predicted time to reach the threshold value, wherein the predicted time is predicted using a trained machine learning (ML) algorithm using historical and present data ingestion rates;
• and
• further triggering the key rotation after a defined time period if the automatic triggering does not occur within the defined time period.