Governing AI Across its Lifecycle: A Framework for Risk Practitioners
This guidance for risk practitioners offers a robust framework for managing the complexities of AI across its entire lifecycle. Unlike traditional technologies, AI systems exhibit non-deterministic behaviors, data drift and evolving outputs, necessitating governance models beyond static control frameworks. This toolkit identifies five critical governance stages—strategy and governance design, development and validation, deployment and go-live, ongoing monitoring and oversight, and change, third-party review, and retirement. By focusing on these stages, organizations can establish tangible artifacts, such as documented decisions and monitoring reports, to ensure defensible oversight and effectively manage AI risks.
Emphasizing the need for comprehensive lifecycle governance, the framework guides organizations to move beyond mere policy establishment to active, cross-functional coordination and accountability at each stage. It points out common pitfalls, like ambiguous post-deployment ownership and lack of structured monitoring, that often lead to failures and regulatory scrutiny. The toolkit serves as an orientation tool to aid risk professionals in transitioning from periodic reviews to continuous oversight, aligning AI governance with evolving regulatory demands and board expectations. Ultimately, it supports organizations in transforming AI risk management from theoretical exercises into practical applications, ensuring that oversight mechanisms are not only established but actively operational.
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