SECAI Core · phase 3 of 15
Data storage
The hardware and software systems used to save, organize, and manage the data needed for training, validating, deploying, and operating AI models, including datasets, model parameters, logs, and related metadata
The Explain card
- Plain English
- Data storage is the hardware and software that saves and manages everything an AI system needs: training datasets, model weights, logs, and the metadata around them.
- Example
- A company keeps its training corpus in object storage buckets, model checkpoints on a network file share, and inference logs in a database.
- Why it matters
- Model weights are crown jewels and training data often contains personal information. A misconfigured bucket can leak both. Storage access controls, encryption and integrity checks are where AI security starts.
- Hook
- The model lives on disk long before it lives in memory.
Where it sits in the deck
Phase 3: Data Fundamentals: Types, Pipelines, and Preparation
Models are only as good as their data — understand the raw material and the engineering that shapes it before covering how it flows or breaks.
