DLP tools were built for files, not prompts. They redact text but ignore who is asking, what action they intend, or whether an agent tool call should be allowed. Agenvia closes that gap with a platform built specifically for LLM-era security.
Trust is not a marketing claim — it is a set of runtime decisions made on every request, logged in full, and improvable across tenants without centralizing sensitive data.
Access is enforced at three dimensions — who you are, what domain you operate in, and what action tier you are requesting. No trust is assumed.
Transformer-based NER, FL-promoted patterns, and multilingual attack signatures catch sensitive data and injection attempts that rule-based tools miss.
Only minimum-viable context crosses the model boundary. Named entities are replaced, strategy is stripped, and the task value is preserved.
Every model response, tool output, and agent step is inspected before it reaches people or downstream systems. Leakage is blocked at the exit.
Agent tool calls are authorized against action-tier ceilings and resource policies. Sensitive operations require human approval before execution.
Privacy intelligence improves across tenants through federated learning with HMAC-signed updates and differential privacy aggregation. No raw data leaves any node.