A blog about Product Management, Data Management and Self-Driven Learning
Most organisations need tailored incident response plans for AI-related issues, including harmful outputs and misuse of data, alongside traditional incident types. Establishing specific scenarios, clear reporting pathways, and effective logging is crucial. Organisations should prepare for incidents proactively, fostering a feedback loop to refine responses and enhance overall safety and governance.
Effective AI governance hinges on explicit oversight modes, including Human-in-the-loop (HITL), Human-on-the-loop (HOTL), and automated systems. Each mode serves distinct use cases based on impact level. Proper documentation, data acquisition, and structured workflows are essential to ensure accountability and transparency, moving beyond vague assurances of human involvement.
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 