Entro Launches Industry First GenAI Engine for NHI Security & Secrets Scanning

Transforms secrets detection and non-human identity risks into human readable, context-rich explanations, demonstrated at RSAC 2025

Entro Security, a pioneer and global leader in non-human identity (NHI) and secrets security, today unveiled a set of generative AI (GenAI) capabilities that bring more context, clarity and control to exposed secrets and NHI-related risks across enterprise environments. This announcement comes ahead of RSA Conference 2025, underscoring Entro’s continued innovation and leadership in NHI security.

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.

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Key Benefits

• Get the full story: Entro’s platform now leverages explainability to provide generated summaries for secrets findings – classifying the target service (e.g., AWS, Slack, OpenAI), environment (production/development/staging), 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.
• Automatically reduce noise: The GenAI engine significantly improves the platform’s false positives detection using advanced reasoning and context analysis, helping customers focus on the risks that really matter and dramatically reduce alert fatigue.
• Enable smarter and faster remediation: Ambiguous and “generic” findings are now enriched with labels GenAI:TP (true positive) or GenAI:FP (false positive), and include explanations from the model. These tags also support inventory search audit workflows and risk filtering at scale.
• Built for scale and compliance: The engine runs on a self-hosted, private LLM stack, ensuring that no secret or NHI content is ever sent externally or stored. Customers can choose the processing region to meet compliance and regulatory requirements – or opt out entirely.

“Entro pioneered visibility and context for non-human identities and secrets – now we’re taking it further,” said Itzik Alvas, CEO and co-founder of Entro Security. “With GenAI, we’re bringing reasoning to every finding. Not just the context of what was exposed, but what it means, how it works and why it matters. This is a new standard in NHI security.”

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