Generative artificial intelligence (gen AI) systems that leverage large language models (LLMs) are proliferating faster than even the most ardent tech enthusiasts had envisioned. Many legal professionals are captivated by this technology and are busy analyzing and discussing the benefits, risks and impacts of gen AI to both the business and the practice of law. Much attention has been dedicated to the use of gen AI for document summarization, categorization, search, and first-draft generation. Almost every practice area is busy assessing the impact of this technology on their respective fields, including the areas of e-discovery and information governance. One area that has not received as much attention as others, however, is the risk involved in business users’ use of gen AI to query data stored in databases.

Harnessing Data in Corporate Database Reporting

Databases serve as the backbone of modern business, allowing organizations to keep pace with the storage, management and analysis of an ever-growing amount of data. While scaling to meet storage needs of the future, these systems also offer data integrity, fast retrieval, scalability, security, and analytics. But these advantages require specialized knowledge to appropriately query the stored content—specialized knowledge that is typically the provenance of database administrators (DBAs), data engineers, and other power users. For example, the most widely used type of database—relational databases—require knowledge of structured query language (SQL) to access the contents stored within them.