A blog about Product Management, Data Management and Self-Driven Learning
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.
In Generative AI, the concept of "Acquire" extends beyond training data to include fine-tuning, retrieval contexts, and prompts. Effective governance is crucial to prevent issues like IP leakage and bias. A structured approach involves defining data usage policies, curating knowledge sources, and treating prompts as governed assets, ensuring safety and compliance in AI initiatives.
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 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.
Nigel D'Souza 