Encryption Key Rotation Using Predicted Data Ingest Thresholds
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Solution Overview
Problem
Current key rotation methods in data encryption systems fail to evenly distribute data across encryption keys, leading to uneven data exposure and varying processing times in case of key compromise, due to varying data ingest and deduplication rates.
Innovation Solution
Implementing an AI/ML-based key rotation system that predicts data ingest rates to determine when a size threshold will be reached, ensuring each key encrypts equal or similar amounts of data, with a backup time-based rotation process for failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time-based key rotation is used, then key rotation occurs at fixed intervals, but data distribution across keys becomes uneven due to varying data ingest rates
Solution Approach 1:
The patent changes the rotation trigger parameter from time-based to size-based, using data ingest rate monitoring to determine when to rotate keys. This ensures that keys are rotated based on actual data volume processed rather than fixed time intervals, achieving uniform data distribution across keys despite varying ingest rates
Solution Approach 2:
The system dynamically adjusts key rotation timing based on real-time data ingest rates. Instead of static time intervals, the rotation schedule adapts to actual data flow conditions, making the rotation mechanism responsive to system state changes and ensuring consistent security posture
2Manufacturing precision
If size-based key rotation is used, then data distribution across keys improves, but processing time varies due to varying data ingest rates
Solution Approach 1:
The system proactively rotates keys based on predicted data volume thresholds before data distribution becomes highly uneven. By monitoring data ingest rates and predicting when the next rotation should occur, the system performs rotations in advance to maintain uniform data distribution while avoiding emergency rotations that would increase processing time
Solution Approach 2:
The system continuously monitors data ingest rates and uses this feedback to adjust key rotation timing. This closed-loop control ensures that rotations occur at optimal moments to maintain uniform data distribution while minimizing disruption to processing operations
3Reliability
If keys are rotated frequently, then security is improved by limiting data exposure, but system complexity and overhead increase
Solution Approach 1:
The system autonomously manages key rotation based on automated monitoring of data ingest rates and prediction of threshold conditions. The self-service mechanism eliminates manual intervention requirements, reducing operational complexity while maintaining security through consistent, data-driven rotation decisions
Solution Approach 2:
By predicting when data volume thresholds will be reached and proactively rotating keys in advance, the system prevents security risks from accumulating while avoiding the need for overly frequent rotations. This predictive approach optimizes the balance between security and operational simplicity
Data Source
AI summary
Rotating encryption keys in a size-based process using data ingestion rates to predict when a threshold size to trigger key rotation will be reached such that an equal amount of data is encrypted by different encryption keys. A machine learning (ML) model is trained based on historical data of data ingestion rates in the data storage system to predict the time that the threshold will be reached. The encryption key is then rotated at that time. A time-based rotation is triggered after a defined period to automatically rotate the key if the size-based rotation is not triggered within this period.