- Validate data source authenticity
- Scan for PII before ingestion
- Check data integrity (checksums, signatures)
- Log all data entering the pipeline
- Run deduplication to reduce memorization risk
- Apply quality filters with documented criteria
- PII detection and redaction
- Bias assessment on processed dataset
- Version control for all processed datasets
- Isolated training environment (no internet access during training)
- Training job authentication and authorization
- Hyperparameter and configuration version control
- Training metric monitoring for anomalies
- Checkpoint signing and integrity verification
- Safety benchmarks before promotion to staging
- Red team evaluation at defined gates
- Performance regression testing
- Bias and fairness evaluation
- Hallucination rate measurement
- Model artifact signing and verification
- Blue-green or canary deployment pattern
- Rollback capability to previous model version
- System prompt change management process
- Production monitoring activated before traffic routing
- Input/output filtering active
- Rate limiting enforced
- Logging and monitoring operational
- Circuit breakers configured
- Fallback path tested