Navigating LLMs challenges in data security & compliance

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In this episode, we discuss the profound impact of large language models, likened to the Industrial Revolution. We delve into challenges related to manual compliance efforts and advocate for automating security processes. The conversation highlights security concerns tied to AI model usage, emphasizing the importance of securing data in advance. Practical advice is shared, urging proactive measures such as automating compliance and staying vigilant amidst evolving security challenges.

Sylvain welcomes Teleskope co-founder Julie Trias Co-Founder, and Elizabeth Nammour. Teleskope is a modern data protection platform that automates data security, privacy, and compliance at scale.

Key takeaways:

  1. Secure Data Before Sending to AI Models: Prioritize securing sensitive information before utilizing AI models to redact personal and sensitive data, ensuring privacy and compliance.
  2. Automate Compliance Processes: Consider automating compliance processes to reduce manual overhead and ensure scalability, especially in the face of evolving regulations.
  3. Be Mindful of Security Challenges: Acknowledge and address security challenges posed by large language models, such as potential data leaks, intentional injection of malicious content, and privacy issues.
  4. Host AI Models On-Premises: Opt for hosting AI models on-premises to enhance control over data and minimize the risk of security breaches and compliance violations.
  5. Evaluate and Choose Tools Wisely: When dealing with generative AI, carefully evaluate and choose tools that prioritize developer-friendliness, minimize false positives, and align with security and compliance requirements.