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Building a Control Framework for the AI SDLC

Since November, Kosli’s own engineering team has been running a live experiment: what happens to code review when the thing generating the code - and increasingly, the thing reviewing it - is an AI, not a person. Alex Kantor, Kosli’s Director of Technology, walked through that experiment in this webinar: what broke, what it cost to fix, and what four “obvious” assumptions in a standard code review control turned out not to hold once you took the human out of the loop.

Building a Control Framework for the AI SDLC

AI coding tools are in use across regulated industries, producing workable code faster than humans can review. It’s a shift that’s raising difficult questions that haven’t been fully answered yet. What does governance look like when AI agents are writing the code, reviewing each other’s work, and fixing their own findings? How do you prove what code is running and whether it passed your checks? Who’s accountable for change approvals, audits, incidents?

Introducing Controls in Kosli

Defining a control in a policy document is easy. Proving that every build was evaluated against it - and that nothing non-compliant ever reached production - is the hard part. Kosli's new Controls feature closes that gap. Every control is defined in Kosli, every build is evaluated against it, and deployments are gated automatically when the evidence isn't there. That means: Every control decision is recorded as evidence, build by build Coverage shows exactly where a control is enforced across your environments.

How Norsk Tipping uses feature flags to govern their deployments

Norsk Tipping is Norway’s state-owned gaming operator, running 2,500 to 3,000 production releases a year across iOS, Android, web and backend systems. Like every regulated organisation at scale, the platform team has to hold two things in tension: maintain strict deployment controls that stand up to audit, and keep the path to production open so that 100 engineers can ship safely.