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Protect Your Sensitive Data from Public LLMs

Don’t let your employees upload sensitive data into generative AI tools like ChatGPT.

Marketing team drafting new content

Challenges

  • A marketing team is using public generative AI models to help create new content. They copy company data from documents, some sensitive and some not, and upload it into the models. While the content being created is good, it puts the company at risk since now anyone in the world using that public LLM has access to the data. The company wants the marketing team to continue using the generative AI model, but not at the risk of a data breach.

Solutions

  • While creating a private, internal LLM can mitigate this risk, a better approach is to protect the data itself using Fasoo Enterprise DRM (FED). FED encrypts the sensitive data so it can’t be uploaded or copied and pasted into the public LLM. It places controls and strong security closest to what needs protection – the file – and binds them so safeguards travel everywhere with the file. The sensitive data is persistently protected, visibility is never lost, and policies are there for the life of the document. FED lets organizations take control with granular rights that limit how an insider uses your sensitive data. Since you protect and control all documents with sensitive data, you can easily control what gets uploaded into a public LLM.
ico_use_case_benefits

Benefits

  • Encrypt all files and documents with sensitive information
  • Control who can View, Edit, Print, Copy, Paste, and take a Screen Capture of sensitive files
  • Prevent users from uploading sensitive data into a public LLM
  • Allow use of public LLMs to generate content without compromising security and privacy
  • Protect intellectual property so it can’t be exploited by competitors
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Find out what Fasoo can do for you

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EVENT

iSMG Data Security Summit: Dallas

May 15, 2025
Renaissance Dallas at Plano Legacy West Hotel

Join us at our session to learn more about data security, privacy, and governance in the age of LLMs.

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