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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

research findings AI data security AgentMesh

The Source Looked Clean. The Binary Wasn't.

 
Sample api-reactor.vsix — NoahBit.api-reactor v0.0.1 SHA-256 ca272b481f630635cd059f85321dbc7be372e75820536ccbfd29dfcf571ab45c Publisher NoahBit (GitHub: Noaho950) Marketplace VS ...

OpenAnt: Read the LLM Vulnerability Paper

 
Knostic's OpenAnt, the first and leading open source LLM vulnerability discovery project, is now on arXiv. Traditional static analysis produces too many false positives because ...

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.