A blog about Product Management, Data Management and Self-Driven Learning
The content discusses the integration of Generative AI into machine learning (ML) governance, emphasising the importance of the Design, Acquire, Store, Use, and Delete stages in the ML lifecycle. It highlights governance practices crucial for responsible AI deployment and how existing frameworks can guide the transition to more complex AI systems.
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.
The post discusses the importance of quality data in AI product management, outlining the DASUD model: Design, Acquire, Store, Use, and Delete. It emphasises defining success, collecting accurate data, securely storing it, ethical use, and timely deletion. Trust in data is crucial for effective AI implementation, ensuring good governance.
Free apps often seem costless, but they exploit personal data as their main revenue source. Users unknowingly grant access to sensitive information, which can be sold or used for targeted advertising. To safeguard privacy, it's crucial to review app permissions, read privacy policies, and choose privacy-focused alternatives.
Nigel D'Souza 