These are also closely related to: "The AI-Enabled Trusted Research Environment"

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  Guidelines For Secure Development And Deployment Of AI Systems

  By: Kaspersky

  Type: eBook

  As AI adoption accelerates, organizations must prioritize cybersecurity to mitigate emerging risks. This guide outlines the evolving AI threat landscape, including model vulnerabilities, adversarial attacks, and traditional security weaknesses.

  Guidelines for securing AI systems include:

  • Cybersecurity awareness and training
  • Threat modeling and risk assessment
  • Robust infrastructure security, especially in cloud environments
  • Supply chain integrity and data protection

  Adopt a security-first approach to AI.

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  How do we get started using LLMs? This guide lights the way

  By: DataBricks

  Type: eBook

  Generative AI is taking the world by storm. However, one of the first challenges organizations face in deploying large language models (LLMs) is how to make LLMs understand their proprietary enterprise data. Retrieval augmented generation (RAG) is the leading technique for enhancing LLMs with enterprise data. For example, to ensure chatbots that are powered by LLMs are responding with accurate, relevant responses, companies use RAG to give LLMs domain-specific knowledge drawn from user manuals or support documents.

  This compact guide to RAG will explain how to build a generative AI application using LLMs that have been augmented with enterprise data.

   

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