The author suggests a proactive solution in the form of robust data governance procedures. This involves identifying, classifying, and tagging datasets with sensitive or regulated information. The author also recommends using solutions that can scale, have enterprise-grade security, and can cover the range and scope of their data. The article concludes by stating that as AI and ML technologies continue to evolve, proactive data governance will become a crucial part of responsible business operation.
Key takeaways:
- Unstructured data, while a powerful input for AI-driven solutions, can pose a risk due to its chaotic state and the sensitive information it often contains.
- Large language models (LLMs) trained on unstructured data can amplify risks, including violating data privacy regulations and exposing organizations to data breaches.
- Before deploying generative AI, it's crucial to establish robust data governance procedures, including identifying, classifying, and tagging datasets that contain sensitive or regulated information.
- As AI and ML technologies continue to evolve, proactive data governance will become a nonnegotiable facet of responsible business operation, and failure to manage the risks associated with unstructured data can have serious legal and financial repercussions.