From governance principles to technical controls

Perfection isn’t the goal — prioritization is. “CIOs don’t have to excel in every aspect of governance, just excel at governing non-negotiables like high-risk use cases, transformative use cases, or security and privacy,” Gartner’s Kornutick says. CSA’s Baron agrees: “The agents that combine broad access with high autonomy are where your strongest controls and attention should go. ” Beyond prioritization, a few things distinguish AI governance that delivers: It changes decisions. “Does this process ever cause us not to fund something, or to shut something off?

” Jayaprakasam asks. “If every initiative that enters the process comes out the other side approved, you don’t have governance — you have a notarization service. ” It’s an operating system, not a legal memo. The organizations that get governance right maintain a live AI inventory, classify use cases by risk, embed controls into existing workflows, and use technical enforcement — not just written policies, says Jodka. It measures what matters.

At Deluxe, Jayaprakasam relies on what he calls “evals”: a standard set of tasks and scoring rubrics for every AI capability. “Evals are the difference between ‘we believe this is working’ and ‘we can show this is working,’” he says. “They’re also what lets you make a decommissioning call without it turning into a political fight, because the number — not the loudest stakeholder — makes the decision. ” And that work is never finished: “Without measurement, governance is just opinion with a calendar invite,” says Jayaprakasam. It builds on what you have.

Kornutick advises against building governance from scratch. “AI is just another type of technology, and for many organizations, they should just be enhancing existing processes for what is new about AI and what other risks AI amplifies,” she says. The aim, Kornutick adds, is alignment — “everyone rowing the boat in the same direction” — where governance is viewed as a business enabler, not a stop gate.