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.
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.
As a volunteer at Product Camp, I decided to put together this handy little guide for Attending your first Product Camp unconference can be exciting yet nerve-wracking. Embrace the spontaneity, stay open to new experiences, and engage with participants using personal questions. Remember everyone shares similar feelings, so network freely and have fun. Prepare with essentials like water and comfy shoes to enhance your experience.
Public Wi-Fi is convenient but poses significant security risks, including Man-in-the-Middle attacks, fake hotspots, and unencrypted networks. To enhance safety, users should employ VPNs, avoid sensitive activities, verify network legitimacy, and enable two-factor authentication. Staying informed and adopting security measures can help protect personal data while using public Wi-Fi.
Nigel D'Souza 