Generative AI: How should enterprise information governance models respond to its use?

The great thing about generative AI is that it is generative. It results in the creation of new content in an efficient way, drawing on vast sources of data. But with that power comes the potential for bumping into issues that range from intellectual property protection to data privacy concerns to reputation management in the marketplace. Sensible enterprises should implement information governance models that maximize the effectiveness of the use of generative AI while curbing the risk for bad results. Here are some things to keep in mind.

Contextual Understanding and Scope Definition

With the surge in the use of generative AI tools within enterprises, it is important to revisit and recalibrate information governance models. Initially, enterprises should gain a thorough understanding of how generative AI is being used within the organization. This involves identifying the departments, processes, and roles where these AI tools have been implemented. Once the enterprise has a comprehensive view, it should define the scope of its governance model by setting boundaries on the generation and consumption of AI-produced content.

Data Quality and Integrity Checks

Generative AI systems can produce massive volumes of information, and while their efficiency is unparalleled, they are not infallible. Businesses must institute robust data quality and integrity checks to ensure that the outputs from these AI systems align with organizational standards. This might mean setting up periodic audits, incorporating human-in-the-loop validation processes, or using secondary AI tools to assess the reliability of the generated content. There should be mechanisms to track the lineage of AI-generated data, so it is clear where information originates and how it evolves over time.

Training and Responsibility

Employees must be trained adequately not only in the usage of generative AI tools but also in understanding the potential risks and biases associated with them. Proper training ensures that employees can distinguish between AI-generated outputs and human-generated content, understanding the strengths and limitations of each. Additionally, there must be a clear delineation of responsibility. Who takes accountability when generative AI produces inaccurate or harmful information? Assigning roles and responsibilities prevents ambiguities in accountability, ensuring that potential issues are addressed promptly.

Ethical and Legal Considerations

Generative AI poses unique ethical and legal challenges. From creating synthetic data that might be indistinguishable from real, sensitive data, to potentially producing misleading or biased information, there are many potential pitfalls that enterprises must navigate. The governance model should incorporate ethical guidelines on AI use, ensuring transparency, fairness, and privacy. Legal teams should be closely involved to stay updated on regulations that pertain to AI-generated content, ensuring that the organization remains compliant, and is prepared for any potential legal implications. For example, Colorado has a statute that imposes certain requirements on participants in the insurance industry governing how AI tools are used to undertake various insurance practices.

So as generative AI becomes an integral tool for many businesses, the need for robust information governance models becomes paramount. Only through comprehensive, proactive measures can enterprises safely harness the power of generative AI while safeguarding the quality and integrity of their information.