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38 INFORMATION GOVERNANCE AS AN ENABLER OF RESPONSIBLE AI Information governance professionals have long been responsible for managing the lifecycle of information in complex organizational environments. As AI technologies become more integrated into legal practice, that expertise becomes even more valuable. Rather than being viewed solely as a compliance function, IG should be considered an enabler of responsible, scalable AI adoption. Involving governance professionals early helps ensure that innovation efforts align with the firm's broader information management practices. Doing so supports clearer decisions about what data should be made available to AI systems, how that data is governed over time, and how new information generated by those systems fits into existing records and retention frameworks. Without that early involvement, firms risk discovering (often too late) that they cannot confidently answer basic governance questions: where data resides, how it can be destroyed when required, whether ethical walls are respected, or how AI-generated work product should be treated once it enters the firm's information ecosystem. Ethical walls present a particularly instructive example. When AI systems draw from broad internal knowledge repositories, the access restrictions that govern traditional matter workflows may not automatically carry over. Content that would be off- limits to a specific attorney under standard conflict protocols could become accessible through an AI tool that aggregates across matters, creating exposure that neither the technology team nor the business intake function is positioned to anticipate. That is precisely the kind of risk that information governance professionals are trained to see. BRINGING IG INTO THE AI CONVERSATION The rapid pace of AI development means many firms are understandably focused on experimentation and innovation. Governance does not need to slow that momentum. In fact, incorporating information governance early can help organizations scale AI adoption with greater confidence and fewer downstream surprises. Practical steps for integrating IG into AI initiatives include: • Include IG leaders in AI evaluation and governance committees. This ensures lifecycle management and data stewardship considerations are addressed alongside technical and strategic decisions. • Map the information sources AI tools rely on. This builds a foundational inventory of the data ecosystems powering those systems, including how underlying datasets are curated, maintained, and updated over time. • Define governance expectations for AI outputs. This includes whether AI-generated materials constitute work product, records, or transitory content within existing retention frameworks. • Assess how AI systems handle derived or generated content. Recognize that new information created by AI can introduce governance obligations of its own.

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