Review data quality for incorrect labels and representativeness; apply privacy-preserving and responsible data governance (data cleaning, on-device processing, federated learning) and monitor pre-trained models used; consider incremental scaling where a safe model at full scale is uncertain; apply state-of-the-art alignment and safety techniques; and prevent or mitigate risks in data collection, processing and data enrichment services.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.