Design

Mitigation strategy

1. Formalize a governance process to rigorously vet the ML task formulation and the design specifications of all system components (e.g., tokenizers, data pipelines) during the pre-deployment phase to ensure fundamental alignment with robustness and safety requirements. 2. Implement comprehensive, systematic end-to-end validation methodologies, including stress-testing against diverse datasets and simulated failure modes, to empirically verify the functional robustness and reliability of the overall system design before deployment. 3. Establish continuous operational monitoring systems to track critical performance metrics and proactively detect model decay or drift, which serves as an essential mechanism for identifying and correcting failures arising from initial design assumptions no longer holding in the deployment environment.