Implement effective data authorization mechanisms to secure your data used in generative AI applications – part 2

This article discusses implementing effective data authorization mechanisms for generative AI applications, focusing on securing sensitive data across different stages and components of AI systems.

• Data governance is critical for managing access to sensitive data in generative AI applications
• Four key areas of data governance: data visibility, access control, quality assurance, and ownership
• Sensitive data can exist in multiple locations:
  • LLM training and fine-tuning
  • Vector databases
  • Tools
  • Agents
• Authorization methods include:
• Creating separate vector databases for different departments
• Using metadata filtering to control data access
• Implementing application-level authentication and authorization

The article emphasizes that data authorization decisions must be made by the application, not the AI model, to ensure proper data protection.