Inference protection is a preventive approach to LLM privacy that stops sensitive data from ever reaching AI models during training or inference. Unlike post-exposure mitigation, it de-identifies unstructured text (clinical notes, legal documents, customer records) before it enters a model. Since LLMs cannot selectively forget information once absorbed into model weights, preventing exposure upfront is the only reliable way to meet GDPR, HIPAA, and emerging AI regulations. Tonic Textual is presented as a solution that automates this protection layer, enabling organizations to use LLMs on real-world data while maintaining compliance and reducing audit risk.
Table of contents
The challenge for real-time LLM proxiesReal world implicationsA preventative approachEnabling safe model trainingShrinking compliance and operational riskA foundation for responsible AISort: