Before Adopting AI, Implement an AI Governance Framework

An AI governance framework is a critical component of AI adoption, according to a recent LinkedIn post by Malbek’s Colin Levy.  There is a widening gap between adoption and governance. Legal teams are often using AI without fully evaluating or approving its embedded role.

The issue here is a structural challenge. Rather than standalone purchases, AI capabilities are bundled into platform updates and renewals, leading to governance questions that often arise after the technology has been implemented. This dynamic means that governance must be addressed first. 

Many organizations begin with acceptable use policies, but these only establish baseline permissions. Effective governance requires deeper operational clarity, including defined review standards, accountability for AI-generated work, and thresholds for what constitutes sufficient quality across different legal outputs.

A critical but underdeveloped component of an AI governance framework is maintaining a living inventory of AI usage. Without a clear map of which tools interact with which tasks or users, legal departments cannot accurately assess exposure or risk. This gap becomes particularly significant in areas of privilege and confidentiality.

Design decisions within AI-enabled systems further complicate governance. Tools that obscure missing information or make assumptions can shape legal judgment without transparency. Governance must therefore extend into system configuration, ensuring oversight and auditability are embedded into how tools function, not just described in policies.

Finally, governance must span the full lifecycle of AI use, including exit planning. Decisions influenced by AI must remain explainable long after a tool is retired, requiring foresight around data retention, audit trails, and documentation, thus making governance an ongoing discipline.