Generative AI governance: what enterprises get wrong
Most generative AI governance frameworks focus on what to block. The more useful question is what to enable safely, and how.
Early generative AI governance efforts at most enterprises focused heavily on restriction: blocking public tools, restricting use cases, and routing every request through lengthy approval processes. This reduces some risk, but it also pushes usage underground, where it's far harder to govern at all.
A more effective approach starts by classifying use cases by risk and data sensitivity, then defining what's allowed by default at each tier, rather than requiring approval for every individual request.
Technical controls matter as much as policy. Data loss prevention at the point where information leaves the organization, audit logging of model interactions, and retrieval architectures that keep sensitive data inside the company's own environment all reduce risk more effectively than a policy document alone.
Governance frameworks that enterprises actually sustain tend to be revisited quarterly, not written once and left static. The technology and the threat landscape are both moving quickly enough that a governance approach from a year ago is likely already out of date.