Shadow AI Detection: Once you identify all sanctioned AI models across your organization, you need to actively search for and include “shadow AI” — AI models and applications that have been developed or deployed without formal approval or oversight. This often includes models developed by individual departments or teams for specific, localized purposes.
Catalog Creation: You need to compile the documented information into a centralized AI model catalog. This catalog should be accessible and understandable, serving as a comprehensive reference for the organization’s AI landscape.
Review and Validation: Finally, you need to regularly review and validate the AI model catalog to ensure it remains accurate and up-to-date. This includes adding new models, updating model details, and removing obsolete models.
By completing this step, businesses can break down silos and shed light on the entirety of their AI landscape. This not only enhances transparency and governance but also facilitates better decision-making and risk management regarding AI deployments. Moreover, understanding the full spectrum of AI applications within the organization paves the way for optimizing resources, avoiding duplication, and leveraging AI more effectively across business operations.
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