A blog about Product Management, Data Management and Self-Driven Learning
Generative AI requires a distinct design approach compared to traditional machine learning, emphasising the need to classify use cases by their impact: informational, decision-support, or action-taking. Organisations should establish guidelines addressing acceptable error levels, prohibited areas, and human oversight, enabling effective management of risks associated with GenAI outputs.
The DASUD framework—Design, Acquire, Store, Use, Delete—serves as a valuable model for AI governance, enhancing existing data management practices. It outlines a structured approach to integrate governance throughout the AI lifecycle, ensuring clarity in decision-making, accountability, and risk management while adapting familiar processes for AI applications.
After launching the DASUD Framework, I am now focused on AI governance, emphasising the importance of understanding fundamental problems to govern efficiently. I highlight the risks of multi-agent systems compromising data security and stress collaborative governance solutions. The piece invites readers to seek guidance on Data Governance and related topics.
Nigel D'Souza 