Anyone else noticed how broken enterprise AI + PII handling actually is?
Summary
The author describes the common enterprise problem where PII redaction before sending data to LLMs breaks the output, and they are building a solution that rehydrates responses without exposing raw data.
Similar Articles
Is AI really the hard part anymore, or is it getting AI to access the right data?
A discussion on whether LLM capability or enterprise data access and permissions is the bigger bottleneck for AI adoption, arguing that content management is becoming as critical as the models themselves.
@liquidai: Sharing confidential information with AIs is a norm these days! We can do better. Our latest encoder models find and st…
Liquid AI's LFM2.5-Encoders can identify and strip 40 types of PII across 16 languages in one pass, designed for secure AI data processing within private pipelines.
1 in 8 AI support prompts contained personal data. I think we're securing LLMs the wrong way.
An analysis of 10,000 production AI support prompts found 12.4% contained personally identifiable information, arguing that LLM security should focus on data minimization and redaction rather than only prompt injection or jailbreaks.
PII data to LLM
Discusses the risks and considerations of sending Personally Identifiable Information (PII) to large language models.
Most AI agents processing sensitive data right now have ZERO documented controls. That's becoming a real problem!
The article highlights a critical gap between the deployment of AI agents handling sensitive data and the lack of compliance and governance controls, with significant regulatory risks like EU fines, and points to solutions like Lyzr's Responsible AI layer for integrated security.