Evolving AI Data Governance to Ensure Compliance and Mitigate Risks

Data integrity requires that AI data governance is prioritized to ensure compliance requirements are met and risk is mitigated as well as acting in a responsible manner.

The AI regulatory bodies are evolving with new discoveries in technologies related to the AI field, and all these new AI applications create sufficient challenges for data governance. Be it machine learning or deep learning or generative AI; however one may put it, at the end, we are stuck with the similar issue of data governance. AI data governance is an essential play role with respect to ensuring compliance, reducing risk and helping to maintain an untarnished data quality.

It would assure that within an organization we are part of the whole emerging perspective about responsible AI regulations and execution of the same by taking into account problems related to model drift, data quality for training, and biased AI models. But the integration of AI governance with an existing data governance framework presents a problem for a lot of organizations. Often AI and governance already existed in different siloes: there was no one to explain to the governance how AI interpreted data privacy and compliance.

For instance, governance for AI such as generative AI models or corporate chatbots needs new rules for data access control to user data privacy and protection from copyright law infringement. There must be a broader inclusion of AI governance into the existing data governance policies. This would need an integration of all AI and data governance teams and spending on governance tools meant for AI to deal with information management, capture the use of AI in work activities, and interfacing policies alongside Machine Learning Operations (MLOps) tools.

Assembling emerging risks with compliance and responsible scaling of AI initiatives while data integrity and security are maintained could be achieved with their IoT enabled cows for AI data governance.