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What does your team actually produce as evidence when AI code goes to production?

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Something I keep coming back to: if an auditor asked your team to prove that the AI-generated code in your last release passed your risk policy — what would you actually hand them?

Most teams I talk to would produce a PR link, maybe a Copilot usage dashboard, and a verbal description of their review process. No record of which model generated which function, no risk score at insertion time, no machine-verifiable proof that a human reviewed the AI context rather than just the diff.

We just shipped an indemnity certificate system in LineageLens. You define a policy (max risk score, allowed models, require human review, license clean), call an endpoint for a specific PR or release, and the system evaluates every provenance record tagged to that scope against your rules. Either it issues a signed Ed25519 certificate — verifiable by any third party without workspace credentials — or it returns a structured list of exactly what failed and why.

The certificate anchors to the provenance hash chain at the moment of issuance, so it's not just "we said it was reviewed." It's a cryptographic statement tied to the capture records that existed at the time.

What I'm genuinely curious about: what does your team produce today as evidence when AI code ships? Is there anything machine-verifiable in that process? And where do you think the enforcement gate should actually live — pre-merge block, or post-merge audit evidence?

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To be honest, I think most startups would struggle to provide anything beyond PRs, commit history, test results, and code reviews.For many teams, AI is already part of the development process, but the governance layer around it is still immature.

The kind of machine-verifiable evidence you're describing sounds more relevant for finance, healthcare, or highly regulated environments than for typical startup teams today.


I'd lean toward pre-merge checks for enforcement. Catching policy violations before code reaches production is usually much cheaper than proving compliance afterward.

 I think that's where the market is today too. Most teams can produce PRs, commits, tests, and review history, but very little of it is actually machine-verifiable AI evidence.

My intuition is that startups may not feel this pain immediately, but as AI-generated code becomes normal, today's "nice-to-have" provenance may become tomorrow's baseline audit expectation. I also agree on pre-merge enforcement—it's usually much easier to prevent policy violations than explain them later.

  1. A friend working with AI generated code mentioned that the hardest part is knowing why a certain change was made. Capturing that context could help a lot.

 I think that's a big part of the challenge. Code diffs tell you what changed, but they rarely explain why the change happened. Once AI becomes part of the workflow, preserving some of that generation context can make debugging, review, and future investigations much easier.

What's your opinion!!