Enterprise
How to Audit AI Generated Code
Audit AI generated code by checking the task boundary, changed files, data flow, dependency changes, tests, security-sensitive paths and reviewer notes. Start with the diff, not the agent summary. The audit should prove what changed and what stayed untouched.
On this page
What controls matter for AI generated code audit?
AI generated code audit is a governance question before it is a tooling one. Four controls decide whether security can sign off: who has access, what the policy allows, how data moves, and whether anyone is measuring adoption. The table assigns each an owner.
- Control
- Identity
- Owner
- IT
- Evidence
- SSOSingle Sign-On. One company login (usually via SAML or OIDC) instead of a separate password per tool. Press Enter for the full definition., SCIMSystem for Cross-domain Identity Management. A standard for automatically creating and removing user accounts when people join or leave. Press Enter for the full definition. or membership source is defined
- Control
- Policy
- Owner
- Engineering leadership
- Evidence
- Allowed repos, tools and review rules are documented
- Control
- Security
- Owner
- Security team
- Evidence
- Data flow, secrets boundary and audit path are reviewed
- Control
- Adoption
- Owner
- DevEx
- Evidence
- Pilot metrics and training path are live
| Control | Owner | Evidence |
|---|---|---|
| Identity | IT | SSOSingle Sign-On. One company login (usually via SAML or OIDC) instead of a separate password per tool. Press Enter for the full definition., SCIMSystem for Cross-domain Identity Management. A standard for automatically creating and removing user accounts when people join or leave. Press Enter for the full definition. or membership source is defined |
| Policy | Engineering leadership | Allowed repos, tools and review rules are documented |
| Security | Security team | Data flow, secrets boundary and audit path are reviewed |
| Adoption | DevEx | Pilot metrics and training path are live |
The owners column matters more than the controls themselves, which I realise is a slightly odd thing to say about a governance table. But a control with nobody's name against it is a control nobody checks, and it fails at exactly the moment someone has to answer for it. The usual mistake is assuming security owns all four. They don't. Identity is IT's system of record, policy is an engineering-leadership call about what the team is allowed to ship, and adoption belongs to whoever owns developer experience. Security owns the data-flow question and the audit path, and they will ask about both.
Data flow is the one that stalls approvals, so answer it before the meeting rather than during it. Write down what the tool can read, what it can change, what leaves your network and where any of it is retained. Most of that comes straight from the vendor's documentation and your own configuration. It's dull work, but it turns a vague objection into a specific one, and specific objections are the kind you can actually close.
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
This is covered hands-on in Cursor First Hour — 4 short modules, free to read.
How should the rollout work?
A rollout works when it starts narrow and earns its expansion. One team, one repo and a few real tasks in week one, with policy and training in place before anyone scales it wider.
- 1Week 1: pick one team, one repo and three realistic tasks.
- 2Week 2: write the workflow standard from the pilot.
- 3Week 3: train champions and add policy guardrails.
- 4Week 4: expand only after quality, cost and review load are visible.
Four weeks is a shape, not a rule. The dates matter much less than the order, and the reason to start with a single team is that those first two weeks are mostly you finding out what your standard should even say. You can't write that from a pilot too broad to watch properly. Pick the team that will tell you when something doesn't work, rather than the team most likely to hand you a good result.
Expansion is where this usually goes wrong. Not because anyone's careless, either — the pilot went well, someone senior noticed, and now there's pressure. That's a hard thing to say no to. Before you add teams, though, check that you can answer three things with numbers instead of impressions: did review load go up or down, what does a developer actually cost per month, did quality hold. If any of those is a shrug, another fortnight of the same pilot beats a rollout you have to walk back.
How do I stop the agent from auditing with confident false positives?
Make it prove every claim before you act on it. A security reviewer's core technique against false positives is to copy the agent's finding straight back and refuse it: in effect, "I don't believe you - explain it in depth, including the attack chain and the relevant code snippets, tell me in detail how this would be exploited, and prove that the exploit works." The agent re-investigates, and a surprising share of findings collapse - often it turns out to be intentional behavior the user invokes themselves.
Push the agent down to docs and code until you and the model reach the same ground truth. This generalizes far past security: it is how you audit any confident agent output.
I'm literally like copy and paste this one and say... explain it in depth including the attack chain relevant code snippets and tell me in detail how this would be exploited and prove that the exploit works.
Are agents better than static scanners for reviewing code?
For tracing context, yes - emphatically. One reviewer answers the agent-vs-traditional-scanner question with "100,000%" for agents. Static rules are static: they can't trace through the surrounding code the way an agent can, so a genuinely good static rule is very hard to write. The viable hybrid is to feed linter and scanner output into the agent and let it triage, with the model choosing the right checks on the fly.
A scanner sees a pattern. An agent can follow that pattern through the call graph and decide whether it's actually reachable.
the problem with any kind of like static rules is that they're static and it doesn't really have context. So it would be very hard to write a good static rule that actually like traced through all the context in the way an agent can.
They make things up, which gives you two problems to manage.
False negatives: mitigate with parallel runs and specific priming so a missed issue in one run gets caught in another.
False positives: mitigate with the validation loop above - make every finding prove itself before it counts.
How do I audit tests the agent wrote?
Never trust a test you didn't see fail. AI generating tests that auto-pass is dangerous, because a test that asserts nothing still passes. You have no proof it guards anything until you have watched it go red. The fix is engineering discipline, not magic.
- 1Explicitly determine the golden state - the happy-path input and the output you expect.
- 2Feed that input/output state to the agent as the test's anchor, or codify it in a detailed plan.
- 3Manually inspect the generated test code to confirm it actually asserts that state.
- 4Make the test fail on purpose at least once, so you know red means red.
Cursor accelerates your engineering process; it does not replace verifying what's actually under test.
Never trust an automated test that you didn't see fail. You write tests based on existing code. This is dangerous... You have no proof.
Frequently asked questions
Who is this guide for?
Security reviewers, staff engineers and teams using AI on production repos.
What should I do next?
Start with one real repo task, capture the prompt and review the result before scaling the workflow.
How do I cut down on confident false positives in an AI security review?
Refuse the finding and make the agent prove it. Copy the claim back and say you don't believe it: ask for the full attack chain, the relevant code snippets, a detailed exploitation path, and proof the exploit works. Many findings collapse under this - they turn out to be intentional behavior. The technique generalizes to auditing any confident agent output.
Should I replace my static scanners with an agent?
Lean on the agent for context-tracing, but keep the scanners. Static rules can't follow context through the code the way an agent can, so agents catch issues scanners miss. Agents also make things up, so feed scanner and linter output into the agent to triage, use parallel runs against false negatives, and a prove-it validation loop against false positives.
Sources & last verified
- Cursor privacy and data governance
- Cursor teams dashboard
- Cursor enterprise
- Cursor agent best practices
Cursor ships frequently. Facts verified against primary sources on July 9, 2026.
Keep reading
Rather do it than read about it? Run 11 interactive Cursor walkthroughs in a simulated editor. Free, no account needed.