What is the purpose of data governance in AI?

Data governance in AI serves the fundamental role of ensuring that data management practices not only comply with relevant laws and regulations but also adhere to established best practices. This encompasses a wide range of activities, including defining data ownership, establishing policies regarding data usage, and implementing security protocols to protect sensitive information. By fostering a strong governance framework, organizations can maintain data quality, integrity, and security, which are critical in AI applications where data is foundational to training models and generating insights.

Ensuring compliance helps organizations mitigate risks associated with data breaches, unauthorized access, and potential legal repercussions stemming from misuse or mishandling of data. It also involves maintaining transparency about how data is collected, stored, and processed, which builds trust among users and stakeholders.

While the other options may relate to aspects of data management, they do not encapsulate the central purpose of data governance, which is primarily focused on compliance and best practices. For instance, enhancing processing speed, improving user-friendliness, and reducing redundancy are important aspects of data management but fall outside the core scope of governance itself.