Entro Security, unveiled a set of GenAI capabilities that bring more context, clarity and control to exposed secrets and NHI-related risks across enterprise environments. The new engine, powered by large language models (LLM), enriches Entro’s security findings with structured, natural language summaries. Each finding is automatically classified based on metadata and context, making it easy for security teams to understand what each NHI does, where exposed secrets live and what’s at risk. This release builds on Entro’s previously launched GenAI ownership attribution model, which automatically assigns a human owner to each exposed secret or NHI using a smart multi-source hierarchy. Together, these capabilities drive faster triage, smarter remediation and clearer accountability across the NHI lifecycle. Entro’s platform now leverages explainability to provide generated summaries for secrets findings – classifying the target service , implementation type, potential purpose and more. Security teams no longer need to chase down vague pattern matches across environments or guess what the unknown secret is doing. The GenAI engine also automatically reduces noise, enables smarter and faster remediation, built for scale and compliance.