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Knostic for Legal Teams

Privileged Docs Deserve Discretion

AI chat can spill litigation strategy, settlement terms, or attorney-client memos to anyone who asks the wrong question.

How Knostic Protects Legal Work-product

Privilege firewall

blocks privileged docs from being surfaced in any LLM response.

Need-to-know enforcement

checks user clearance and legal-hold status before the model can reference sensitive matters.

Breach-path testing

lets you simulate prompts to see exactly what Copilot might leak, before a real user tries.

Full traceability

with detailed exposure results and policy mappings, giving counsel a defensible record for audits, discovery, and compliance reviews.

Explore our latest Security Tools

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Test your LLM for oversharing

Ever wonder what your Copilot or internal LLM might accidentally reveal? We help you test for real-world oversharing risks with role-specific prompts that mimic real workplace questions.

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RAG Security Training Simulator

RAG Security Training Simulator is a free, interactive web app that teaches you how to defend AI systems — especially those using Retrieval-Augmented Generation (RAG) — from prompt injection attacks.

Made for Legal Teams

Safeguard attorney-client privilege, cut discovery risk, and keep every case on solid ground.

Request a Demo

Latest research and news

OpenAnt

Free LLM-based Vulnerability Scans for Open Source Projects

 
AI is transforming vulnerability discovery for attackers and defenders alike. AI agents are now finding and exploiting vulnerabilities autonomously, at machine speed, and the open ...

OpenAnt: Open Sourcing Knostic’s LLM-based Vulnerability ...

 
Introducing OpenAnt OpenAnt from Knostic is an LLM-based vulnerability discovery product that helps defenders proactively find verified security flaws while minimizing both false ...

What’s next?

Want to solve oversharing in your enterprise AI search?
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Knostic is the comprehensive impartial solution to stop data leakage.

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Knostic offers visibility into how LLMs expose your data - fast.