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P E E R T O P E E R M A G A Z I N E · S P R I N G 2 0 2 6 37 patterns, and synthesize information into new outputs. In doing so, they often interact with a wide range of internal content, including documents, emails, research materials, and other work product that collectively represent a firm's institutional knowledge. For law firms, this dynamic introduces new governance considerations. AI systems do not simply access information; they interpret it, combine it, and generate derivative content that may influence how knowledge is reused across the organization. As these technologies become more integrated into daily legal workflows, firms must consider not only the capabilities of the tools themselves but also how those tools interact with the broader information ecosystem. These shifts do not necessarily introduce entirely new risks. Rather, they have the potential to amplify existing information management challenges. This makes gaps in data quality, retention practices, access controls, and lifecycle discipline more visible and more consequential if governance considerations are not included early. THE COMPLEXITY OF LEGAL DATA Law firms operate in uniquely complex information environments. Unlike many corporate organizations that rely heavily on structured data systems, legal work is often built on unstructured content, such as contracts, briefs, correspondence, research materials, and work product created across thousands of matters. Over time, these materials accumulate into vast knowledge repositories that represent both the firm's institutional memory and its competitive advantage. Generative AI tools thrive in precisely this environment. The ability to analyze large collections of unstructured content and surface relevant insights is what makes it so compelling for legal professionals. At the same time, this capability introduces new considerations around how that information is accessed, reused, duplicated, and governed over time. Understanding how AI systems interact with the firm's information landscape, including how datasets are curated, maintained, and evolve, is a critical component of responsible AI adoption. THE MISSING VOICE IN AI DISCUSSIONS Across the legal industry, conversations about responsible AI adoption often focus on important topics such as cybersecurity, privacy, and ethical use. These issues rightly receive significant attention. However, the lifecycle management of the underlying data -- the policies and practices governing how information is retained, organized, and ultimately disposed of -- is sometimes overlooked in broader AI discussions. Even guidance from vendors and industry groups about evaluating legal AI tools frequently emphasizes security and compliance considerations without addressing the governance frameworks needed to manage the data that powers these systems. This is a notable gap. AI tools are only as reliable and responsible as the data they rely on. Without thoughtful governance frameworks in place, firms may find themselves grappling with questions such as: • What information sources are used to train or power AI tools, and how are those datasets curated and maintained? • Are duplicate or derivative datasets being created during experimentation or deployment? • How should AI-generated outputs, such as drafts, summaries, and analyses, be classified, retained, or disposed of? Consider a common scenario: an attorney experimenting with a generative AI tool uploads client documents or internal research to generate a draft or summary. That interaction may create derivative content that exists entirely outside the firm's document management system, with no classification, no retention trigger, and no clear disposal path. The governance gap is not hypothetical. It is already happening in most firms, whether it is visible to those responsible for managing information. These are not purely technical questions. They are governance questions, ones that information governance professionals are uniquely equipped to help answer. Addressing them early, before AI tools are fully embedded in workflows, gives firms the opportunity to build governance into adoption rather than retrofit it afterward.

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