Definition
A conversational context firewall is an architectural boundary in an AI conversation system that controls which data may enter a language model's context, keeping sensitive information such as credentials, identifiers, or health details out of the model unless a step specifically requires it.
In a voice or chat agent, whatever enters the model's context, from the system prompt and retrieved records to the user's own words, can influence its output, be logged with the prompt, or reach a third-party model provider. A context firewall treats that context as a controlled zone. Sensitive inputs are captured by separate, deterministic components; the model receives only what a given step needs, often a result or a placeholder rather than the raw value; and outputs can be checked before they are spoken.
The idea applies familiar security principles, least privilege and data minimization, to language models. It aligns with HIPAA's minimum-necessary standard for PHI and with state laws that restrict whether health or biometric data may reach a general-purpose AI system at all. The term is not a formal standard, so implementations vary; the practical test is which components can see which data at each step of a conversation.
How Consig handles it
Consig runs conversational context firewalls that keep sensitive data out of the model's reach, giving each AI component only what it needs. Journey.ai's Zero Knowledge Network extends that protection to identity, and the same foundation lets a call capture consent, collect health data, or take in a form without that data passing through the AI. See Security.