Derisking AI by design: How to build risk management into AI development

Senior executives should create a top-down view of how the company will use data, analytics, and AI. This should include a clear statement of the value these tools bring to the organisation, recognition of the associated risks, and clear guidelines and boundaries that can form the basis for more detailed risk-management requirements further down in the organisation. Build on the overarching principles to establish the basic framework for AI risk management. Ensure this covers the full model-development life cycle outlined earlier: ideation, data sourcing, model building and evaluation, industrialisation, and monitoring. Controls should be in place at each stage of the life cycle, so engage early with analytics teams to ensure that the design can be integrated into their existing development approach.