What are the best practices for reviewing AI-generated code for compliance?
the heygrc team
Review AI-generated code against the same bar as human code: the auditor will not ask who wrote a change, only whether the control operated. What changes with agents is volume and failure shape. An agent optimises for the visible task, so control code that reads as friction (a log line, a permission check, a retention limit) is at risk in its diffs, and agents open more pull requests than a human compliance reading can keep up with. Automate the first pass; keep humans on the judgment calls.
Review the diff, not the author
Do not create a softer review lane for agent PRs or a hostile one. The compliance question is identical either way: what did this change do to the controls that live in code? Framing it this way also keeps the process stable as the share of AI-written code grows.
Watch the failure shapes agents repeat
The changes to watch are the ones that are clean by every other measure: a noisy audit log line removed as clutter, a permission check widened to unblock a task, test data seeded from a production export, a retention window cut as a cost fix. Each makes the visible task better and quietly moves a control. Your review checklist, human or automated, should name these shapes explicitly.
Automate the first pass to keep pace
Agents multiply pull requests, and a compliance reading that depends on one careful human becomes the bottleneck or gets skipped. An automated compliance review on every PR keeps the reading in step with the code and reserves human attention for the flagged cases. heygrc is built for exactly this: it reviews each pull request against your selected frameworks and cites the control a change touches.
Record the reading
Keep the compliance verdict attached to the pull request. When a large share of your commits is agent-written, that per-change trail is the evidence that review kept operating at machine speed, which is precisely what an auditor will want to sample.
// integration-test users- const users = require("./prod-export.json")+ const users = makeSyntheticUsers(50) await db.users.insertMany(users)An agent seeding tests from a production export is doing the visible task well and quietly widening where real personal data lives. Synthetic users keep the test and drop the exposure.
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