A blog about Product Management, Data Management and Self-Driven Learning
Generative AI complicates data deletion compared to traditional governance, as it involves multiple artefacts like logs and user memories. Organisations must define clear deletion policies for each artefact, including user-triggered options and emergency controls. Balancing auditability and privacy is crucial, necessitating regular reviews of retention policies for compliance and risk management.
Deploying Generative AI fundamentally alters creation and decision-making processes. Proper governance in its "Use" stage is essential to prevent risks such as hallucinated facts and harmful content. By categorising use cases into risk levels and implementing structured review processes, organisations can ensure safe and effective usage of GenAI technologies.
Generative AI systems retain various types of sensitive data, necessitating expanded governance over storage, memory, and logging practices. Organisations should segment data storage by sensitivity and purpose, implement role-based access controls, and ensure safe logging, embedding, and memory management. Clear user communication and a structured governance matrix are essential for effective oversight.
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.
Nigel D'Souza 