Guide
AI-generated code can move fast, but speed without evidence creates release risk. This guide explains how to capture provenance, cap AI claims at the evidence level, and verify AI-generated changes before they ship.
Coding agents and AI assistants can produce code, tests, and fixes faster than manual development. But the evidence behind those changes — what model was used, what prompt was given, what was reviewed, and whether the fix worked — is often lost. Vibe coding security means governing this fast, conversational mode of development without depending on the developer to remember every detail.
Record the model, prompt context, agent identity, and editing session. Provenance should persist with the change so reviewers can reconstruct how the code was produced. An AI-BOM or equivalent record is a practical starting point.
AI-generated changes should not merge without reviewer attestation. The review record should include what was reviewed, by whom, and against what policy. Automated checks alone are not review evidence.
Treat AI-generated claims as probabilistic inputs. An AI assertion that code is safe is a detected-level claim. Promotion requires independent corroboration from tests, scanners, or reviewer attestation.
A validated autofix requires evidence that the original risk is absent. Re-run the original test or scanner, compare outcomes, and preserve the result. Indeterminate outcomes must be escalated, not auto-promoted to fixed.
| Stage | Minimum evidence | Why it matters |
|---|---|---|
| IDE / agent | Model, prompt, agent identity | Reconstruct how the change was produced |
| MCP server | Tool calls and returned context | Show what the agent accessed |
| Repository | Diff and reviewer attestation | Prove a human reviewed the change |
| CI | Test and scanner results on the new artifact | Corroborate AI claims with non-probabilistic evidence |
| Release | Policy evaluation and decision record | Preserve the basis for shipping |
Capture provenance. Record the model, prompt context, agent identity, and editing session so reviewers can reconstruct how the change was produced. Without provenance, AI-generated code cannot be meaningfully verified.
No, not without independent verification. A validated autofix requires evidence that the original risk is no longer present. Compilation, test pass, or ticket closure are necessary but not sufficient.
Agent-neutral governance means security controls do not depend on a single IDE, coding agent, MCP server, repo, scanner, or CI platform. Evidence is captured and evaluated consistently regardless of which tool produced the change.
GoSentrix caps AI-generated claims at the detected evidence level. They can contribute to corroboration but cannot terminate verification without independent non-probabilistic evidence.
Governance for AI-generated code, coding agents, and autonomous workflows.
Governance considerations for Model Context Protocol servers.
Why an AI bill of materials matters for agentic software delivery.
Verify that a claimed fix actually removed risk.
GoSentrix provides agent-neutral governance across IDEs, MCP servers, repos, CI, cloud, and runtime.
