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A follow-up post summarizing the 4 hardest edge cases that break AI agent data sanitization in production — schema drift, allowlist rot, answer-quality degradation, and cross-tool composite PII — plus the hybrid architecture lessons learned from each.
This paper analyzes the privacy-utility trade-off in LLM interactions, uncovering underlying mechanisms and introducing an intent-driven local protection framework with a lightweight model to enhance privacy while maintaining response utility.
OpenAI released a 1B-parameter Apache-2.0 MoE model that strips sensitive data before it reaches any LLM, enabling fully local, leak-proof workflows.
OpenAI releases Privacy Filter, a 1.5B parameter bidirectional token classification model for PII detection and masking, featuring an Apache 2.0 license and long-context support for high-throughput data sanitization.