How to make AI reduce duplicate file storage

AI systems reduce duplicate file storage by analyzing file content and metadata to identify redundant copies. This process involves content-based identification followed by automated deduplication actions.

Key methods include generating unique digital fingerprints (hashes like MD5, SHA-256) for identical detection and employing similarity algorithms (e.g., perceptual hashing, NLP models) for near-duplicates. AI compares these fingerprints across datasets and considers metadata (filename, creation date, size). Processing occurs during uploads ("inline") or on stored data ("post-process"). Accuracy relies heavily on the chosen algorithm and quality training data.

Implementation requires designing a workflow: choose the deployment method (inline for prevention or post-process for cleanup), select appropriate identification algorithms based on file types (hashing for binaries, NLP for text), validate detection accuracy with tests, and define deduplication rules (e.g., keep the latest version). Integrating this into storage systems enables automatic detection and removal or blocking of duplicates, optimizing storage utilization and reducing costs.