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AI Data Governance

This project is an open source AI data governance tool designed to assist organizations in managing and maintaining their data assets to ensure data quality, consistency, and security. The tool is developed based on the NIST ARMF (Assessment, Authorization, and Monitoring Framework) and provides the following key features:

  • Risk Assessment: Assessing the confidentiality, integrity, and availability of data using the NIST 800-53 standard and other best practices, identifying potential threats and vulnerabilities to the data.

  • Authorization: Determining appropriate security controls and policies based on the assessment results and implementing these controls and policies to protect the data.

  • Monitoring: Real-time monitoring of the data, auditing data usage and access, and identifying and correcting anomalous activities.

In addition, the project provides AI data governance methodologies, documentation, and more, including:

  • Data Asset Management: How to classify, label, and manage data for better data protection.

  • Data Risk Assessment: How to assess the confidentiality, integrity, and availability of data, identify potential threats and vulnerabilities to the data.

  • Security Controls and Policies: How to determine appropriate security controls and policies and implement these controls and policies to protect the data.

  • Data Monitoring and Auditing: How to monitor data in real-time, audit data usage and access, and identify and correct anomalous activities.

Documentation and Methodologies

The project provides a series of documentation and methodologies to help you better understand the concepts and best practices of AI data governance. Here are some reference materials:

  • NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0)

  • ISO/IEC DIS 5259-1 Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 1: Overview, terminology, and examples

  • ISO/IEC DIS 5259-2 Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures

  • DAMA-DMBOK: Data Management Body of Knowledge: 2nd Edition by International, DAMA Published by Technics Publications (2017) ISBN 10: 1634622340 ISBN 13: 9781634622349