How to wear Model Armor 3: How you protect sensitive data from LLM. Do you need to?

Google Cloud's Sensitive Data Protection and Model Armor can help enterprises balance protecting sensitive data with maintaining LLM functionality, according to a blog post by Leo Y. The post argues that aggressively blocking all sensitive data harms internal reasoning, user experience, and downstream autonomous systems, and advises selective de-identification.

When building Generative AI applications for the enterprise, you are likely hyper-aware of the risks surrounding sensitive data. The knee-jerk reaction for many engineering teams is to aggressively block all sensitive data ‒ ranging from PII to financial records and internal credentials ‒ from absolutely everywhere in the stack. But a blind, one-size-fits-all approach introduces its own massive headaches. It compromises internal reasoning, ruins user experiences and breaks downstream autonomous systems. As I promised in the first “ How to wear Model Armor https://leoy.blog/posts/how-to-wear-model-armor-1/ ” post, we are going to dive into exactly when to keep sensitive data out of your language models, the reasons you must often avoid de-identifying an agent’s output, and how to orchestrate this balance using Google Cloud’s Sensitive Data Protection SDP and Model Armor.