Archived models must be retrievable for the full ten-year retention period mandated by Article 18. This is not merely a storage requirement; it is a retrieval requirement. The organisation must be able to load, inspect, and if necessary re-evaluate a model that was archived years ago.

The registry’s underlying storage must be durable, with replication and backup to protect against data loss. The storage tier must balance cost against retrieval latency: deep archive storage (S3 Glacier Deep Archive, Azure Archive) is cost-effective but may have retrieval times measured in hours. For models that may need to be retrieved for incident response, a storage tier with faster retrieval is appropriate, at least for the most recent archived versions.

The retrieval process must account for framework versioning. A model serialised with PyTorch 1.x may not load with PyTorch 3.x a decade later. Organisations should consider archiving the container image alongside the model artefact, preserving the complete runtime environment needed to load and execute the model. The ten-year archive infrastructure should be tested periodically by retrieving a sample of archived models and confirming they can be loaded and evaluated.

Key outputs

• Archive storage with ten-year durability guarantees
• Retrieval latency specification per archive tier
• Container image archival alongside model artefacts (recommended)
• Periodic retrieval testing with retained results