Performance targets for AI components, especially reliability targets, are set out clearly for the operating context and proven to a confidence level matched to the risk of failure. Where AI complements or replaces an existing software, human or combined approach, existing targets are reviewed for continuing acceptability, and where acceptable a Globally At Least Equivalent argument may be used. Appropriate responses to reasonably expected inputs outside the intended design are defined and demonstrated.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.