Model Decommissioning

The initial step involves systematically redirecting inference requests away from the target model. This is achieved through load balancer weight adjustment, gradually shifting 100% of traffic to a successor model or a static fallback. Techniques like DNS cutover or API gateway route updates ensure no requests are lost. A deprecation window is often observed first, returning HTTP 4xx warnings to clients still calling the deprecated endpoint before the final shutdown.

To preserve decision provenance for auditability, all model artifacts must be archived immutably. This includes:

• Model Weights: Serialized parameters frozen in formats like ONNX or Safetensors.
• Environment: Container images and dependency manifests.
• Metadata: Training hyperparameters, evaluation metrics, and the Model Card. These artifacts are moved to cold, write-once-read-many (WORM) storage like AWS S3 Glacier Deep Archive to satisfy AI Audit Trail Immutability requirements.

Decommissioning triggers strict Data Governance obligations. The system must execute data retention policies on inference logs, user feedback, and training data. This involves:

• Hard Deletion: Cryptographic erasure of PII from hot databases.
• Retention Holds: Preserving specific datasets required for ongoing litigation or regulatory investigations.
• Lineage Updates: Updating the Data Catalog to mark the dataset as 'archived' and removing it from active training pipelines.

The retired model often serves as an upstream dependency for other systems. Decommissioning requires API versioning deprecation. The Deprecation Window header (Sunset HTTP header) informs downstream consumers of the final shutdown date. Dead Letter Queues must be monitored for stranded messages intended for the retired model, and Circuit Breakers in dependent services must be reconfigured to prevent infinite retries against a non-existent endpoint.

A critical safety step is verifying the Kill Switch mechanism. This ensures that if the model was accidentally left active or if a rollback is impossible, the system can instantly sever the model's ability to act on outputs. This involves testing the emergency stop circuit at the infrastructure level (e.g., scaling replicas to zero) and revoking IAM credentials to prevent any lingering background processes from loading the model into memory.

The final phase is a Blameless Post-Mortem and audit. The Decision Provenance logs are sealed to prove the exact moment the model stopped making decisions. A final Algorithmic Impact Assessment addendum is filed, documenting the reason for retirement (e.g., data drift, business obsolescence, regulatory ban). This step updates the AI System Registration database if required by jurisdictions like the EU AI Act.