Foundational considerations in mitigating AI data risk

Data has become the lifeblood of businesses, and in turn, it is becoming critical for effective artificial intelligence applications. Akin to a living organism, data is generated, enhanced, changed, harvested and reused to build and operate AI systems. As adoption of generative AI and new use cases for large language models grow, organizations must explore pragmatic approaches to navigating their data-related requirements in the pursuit of innovation.

AI risk is multifaceted, but the risks associated with poor data governance controls in particular can have widespread negative business impacts. While each company's unique AI risk profile will vary, some of the data governance challenges they are likely to encounter along the way will be similar. Therefore, for most organizations, establishing a strong foundation of data governance attuned every junction of the AI system life cycle requires a refresh of approaches across data privacy, intellectual property management, contracting, regulatory compliance, information governance and security.

AI-related data risks often result from a limited understanding of the value and sensitivity of the data the company holds, which in turn impacts its ability to protect and appropriately manage that data. While this isn't necessarily a net-new data challenge, the application of AI tools to mismanaged data will inevitably exacerbate the issue. For example, in an environment with an absence of data classification labels and granular access controls, an AI assistant may inadvertently provide some users with unauthorized access to highly confidential data.