What is PAIG?

PAIG (Pronounced similar to paige or payj) is an open-source project designed to protect Generative AI (GenAI) applications by ensuring security, safety, and observability. As the technologies and approaches for writing GenAI applications evolve rapidly, PAIG offers a versatile framework that addresses the latest security and safety challenges and enables the integration of point security and safety solutions without requiring applications to be rewritten.

Overview

The rapid evolution of technologies and approaches for writing GenAI applications is driving the need for robust security and safety measures. New use cases are continually emerging, with users experimenting to discover new possibilities. Despite advancements, there are still many unknowns in the realm of security and safety, necessitating continuous improvement and adaptation. New tools are launched daily to address various aspects of these challenges, often resulting in a fragmented landscape of point solutions. There is a need for flexibility to try out new tools without rewriting applications, customizable solutions to meet specific needs, and future-proofing to ensure long-term relevance. Comprehensive observability is essential to address security, compliance, and operational reviews.

PAIG is an extensible framework designed to implement security and safety in AI applications. It supports and integrates seamlessly with a wide range of Large Language Models (LLMs), Vector Databases (VectorDBs), orchestrators, and other AI tools. As the usage of GenAI evolves, the need for a robust governance framework becomes increasingly important. PAIG addresses this need by providing a comprehensive solution.

PAIG’s Features

  • Extensibility: Seamless integration with a wide range of Large Language Models (LLMs), Vector Databases ( VectorDBs), orchestrators, and other AI tools.
  • Security and Safety: Implements best practices and recommendations from leading security frameworks such as NIST and OWASP.
  • Flexibility: Allows users to integrate new tools and solutions without the need to rewrite their applications, providing a customizable approach to meet specific needs.
  • Observability: Monitors all invocations to LLMs and Retrieval-Augmented Generation (RAG) systems, providing comprehensive observability for security, compliance, and operational reviews.
  • Future-Proofing: Continuously updated to include the latest security and safety solutions, ensuring long-term relevance and protection against emerging threats.
  • User Focus: Helps users concentrate on their core use cases by providing a robust framework that incorporates the best available point solutions.

Why PAIG

PAIG offers a comprehensive solution for securing and safeguarding GenAI applications in a rapidly evolving technological landscape. By providing seamless integration with various AI tools and adhering to industry-leading security practices, PAIG ensures that users can focus on innovation without compromising on safety. Its flexibility and customizability allow for the easy adoption of new tools, while its future-proofing capabilities ensure continued relevance and protection. With comprehensive observability features, PAIG addresses the critical need for security, compliance, and operational transparency, making it an indispensable tool for developers and organizations working with GenAI applications.

Architecture

The PAIG Framework is designed with modularity and scalability in mind. Each layer operates independently, allowing you to customize and extend the functionality to fit your specific needs.

  • Source Metadata ingestion
  • Prevent data leakage in RAG
  • Guardrails for your prompts/responses
PAIG Architecture

Key Features

Metadata Layer

  • Data Extraction: Extract relevant metadata from data sources to enhance understanding of the data and improve model performance as well as apply security, compliance rules
  • Storage: Efficiently store metadata for easy retrieval and analysis.
  • Integration: Seamlessly integrate with various data sources and AI models.

Guardrail Layer

  • Safety Policies: Implement rules and policies to protect users and maintain responsible standards in AI interactions.
  • Content Moderation: Automatically detect and filter inappropriate or harmful content.
  • Privacy and Security: Ensure company data is handled securely preventing data leakage and complies with privacy regulations

Observability Layer

  • Monitoring: Track and visualize AI interactions in real time to ensure system health and performance.
  • Auditing: Maintain detailed logs of user interactions for accountability and compliance.
  • Analytics: Gain insights into user behavior and model performance through comprehensive analytics.

Are you ready to experience PAIG?

PAIG is an open-source Python framework that helps developers build responsible, Generative AI applications.

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