Quick Answer: Choose among three AI code review and security audit platform types by where inference runs: PR review bots, LLM-assisted SAST scanners, or self-hosted audit servers. An enterprise AI code review tool should also export SARIF 2.1.0 findings and let you pick the model. Most regulated teams run a scanner and a review bot together.
Most enterprise evaluations turn less on which model writes the smartest comment than on where your source code goes when a pull request opens, and whether findings land in the security dashboard you already run.
Which platforms review and audit code with LLM support?
Three kinds of platform do this today. Review bots comment on pull requests in plain language. Static analysis (SAST) scanners find vulnerabilities with rules and use an LLM to triage and fix. Self-hosted audit servers run the review inside your own CI and network, often with a model you choose.
| Platform type | What the LLM does | Named examples | Where review runs |
|---|---|---|---|
| PR review bot | Reads the diff and context, writes review comments and suggested changes | GitHub Copilot code review, CodeRabbit, Qodo, Greptile, GitLab Duo Code Review | Vendor cloud by default; CodeRabbit, Qodo and Greptile document self-hosted options |
| SAST scanner with LLM triage | Rules find the issue; the LLM explains, filters false positives and drafts fixes | Snyk Code with Snyk Agent Fix, Semgrep, SonarQube Server AI CodeFix, Amazon Q Developer code reviews | Scan in CI or the vendor's engine; AI features on the vendor platform |
| Self-hosted audit server | Runs rules and LLM review as a CI job, posts findings to the PR and exports SARIF | Self-hosted CodeRabbit, Qodo or Greptile, in-house builds on an LLM gateway, packaged audit servers | Your CI runners and your network, with a model you host or select |
The type matters more than the brand name inside it. A review bot is good at logic slips, missing tests and unclear names. A scanner is good at injection, weak crypto and vulnerable dependencies. A self-hosted server answers the security reviewer's first question: can this run where our code already lives?
How do GitHub Copilot, Snyk, Semgrep and CodeRabbit approach AI code review?
These four are the names buyers ask about most, and each starts from a different place. Copilot and CodeRabbit start from the pull request. Snyk and Semgrep start from security rules and add LLMs on top.
GitHub Copilot code review
GitHub documents that Copilot code review reads code in any language and leaves feedback on pull requests, in the GitHub CLI and in VS Code, Visual Studio and JetBrains IDEs. It's included in Copilot Pro, Pro+, Business and Enterprise, and its agentic context gathering runs on GitHub Actions runners, which can be self-hosted. The same page says model switching isn't supported, and that Copilot isn't guaranteed to spot every problem. Rules-based CodeQL analysis is offered as a separate GitHub Code Quality feature.
Snyk Code and Snyk Agent Fix
Snyk Code is a SAST engine. Its documentation shows it running in CI through a plugin or the Snyk CLI, with JSON or SARIF export. Snyk Agent Fix uses LLMs to draft fixes for Snyk Code findings, and Snyk states it doesn't use customer code to train the models. The model itself isn't named, and model choice isn't documented.
Semgrep
Semgrep runs rules in your pipeline and adds AI triage on its AppSec Platform. The docs describe autotriage that suggests whether a finding can be ignored, noise filtering that holds back PR comments on suspected false positives, and autofix pull requests. OpenAI is the primary model provider, with fallback to Amazon Bedrock; bringing your own model isn't documented.
CodeRabbit
CodeRabbit is a review bot first. Its self-hosted deployment runs the review agent inside your own infrastructure for Enterprise customers with 500 or more seats. It supports GitHub Enterprise Server, GitLab self-managed, Azure DevOps and Bitbucket Data Center, and it can connect to your own LLM provider or account.
| Capability | GitHub Copilot code review | Snyk Code | Semgrep | CodeRabbit |
|---|---|---|---|---|
| Runs in your CI systems | Partly (agentic features use GitHub Actions runners) | Yes (CLI or plugin) | Yes (8 CI providers listed) | Not documented (Git platform app) |
| SARIF export | Not documented | Yes | Yes | Not documented |
| Self-hosting | Not documented for the review service (self-hosted Actions runners supported) | No for new customers (Local Engine deprecated) | Yes for the scan; AI features on the platform | Enterprise tier (500+ seats) |
| Model choice | No (Lite or Balanced effort level only) | Not documented | Not documented | Enterprise tier (your own LLM provider) |
Capabilities as documented by each vendor on 21 September 2026; links in the text.
Choose GitHub Copilot code review when your code already lives on GitHub.com and you want review comments with no new infrastructure. Choose Snyk or Semgrep when security findings must feed an AppSec program. CodeRabbit is the better fit when you want a dedicated review bot across several Git platforms.
Other names buyers compare fit the same types. Qodo, Greptile and GitLab Duo Code Review are review bots: GitLab says Duo reviews merge requests for potential errors and alignment to standards, and Qodo and Greptile document deployment in your own infrastructure. On the scanner side, AWS says Amazon Q Developer reviews use generative AI plus rule-based automatic reasoning, and SonarQube Server lets an admin pick the LLM behind AI CodeFix, including a self-hosted gateway. Faster review doesn't automatically mean faster delivery; how much AI can cut release cycle time looks at the evidence.
What is the difference between AI code review and AI security audit?
Review asks whether a change is correct, clear and maintainable. A security audit asks whether the code can be attacked, and needs evidence an auditor accepts: repeatable rules and a record of every finding.
| AI code review | AI security audit | |
|---|---|---|
| Engine | LLM reading the diff and nearby context | Rules and data-flow analysis, with an LLM for triage and fixes |
| Output | PR comments and suggested changes | Findings with severity, rule ID and location, usually in SARIF |
| Reference | Your team's conventions | Frameworks such as the OWASP Top 10 (2025 edition) |
| Repeatability | Varies run to run | Same input gives the same finding |
For AI code security, repeatability matters most: an auditor will ask why a finding appeared on Tuesday and not Wednesday, and only a rules engine can answer. Use the LLM to explain and prioritize; keep the rule as the record.
Which platforms can run inside your CI/CD and your own network?
Semgrep's scanner, Snyk's CLI and self-hosted audit servers all run as CI jobs. Semgrep documents that its scan runs fully in the CI build environment. The LLM features are the hard part, because they need a model somewhere.
That leaves three data paths for an AI code audit:
- Vendor-hosted model. The scan may run locally, but code snippets go to the vendor's AI service for triage or fixes.
- Your cloud account. The review agent and model endpoint sit in your VPC. Code reaches the model provider only if you point the agent at a hosted API.
- Your hardware, fully offline. The agent calls an open-weight model you host, the only path that works air-gapped.
Whichever path you pick, check that findings come out as SARIF 2.1.0, the OASIS standard for static analysis results. SARIF lets one dashboard collect findings from every tool and survives a vendor change. A pipeline-stage audit also sits under the same change control as your builds, familiar ground for any DevOps team.
How do you evaluate AI code review accuracy on your own codebase?
Measure it on your repositories, not a vendor demo. Replay merged pull requests where you know the right answer and count what each tool catches, misses and flags wrongly.
A workable test set has three parts:
- Known bugs. 20 to 30 past PRs that later needed a fix.
- Planted vulnerabilities. A branch with seeded SQL injection, path traversal, hardcoded secrets and an outdated dependency.
- Clean changes. PRs that shipped without issues; every comment here is noise.
Score precision (comments worth acting on) and recall (real issues found), with two senior engineers grading each comment separately. If you're weighing CodeRabbit alternatives, run every candidate on the same set in the same week, because model updates change results.
How do you trial an AI code review tool on your own repositories?
Run a scoped trial on two or three real repositories for a few sprints, in comment-only mode, with a named owner and exit criteria agreed up front.
- Get security sign-off first. Map the data path: what leaves the network, which model sees it, what is retained.
- Choose representative repos. One busy service, one older codebase, one repo with sensitive logic.
- Tag every comment. Mark it accepted, rejected or ignored to get an acceptance rate.
- Compare with your scanner. Note overlaps and what each alone caught.
- Decide on evidence. Promote to a required check only for rule classes with high precision.
Give each AI code review tool the same tuning budget and record the settings, so the rollout matches what you tested.
What mistakes should you avoid when adopting AI code review?
Most failed rollouts treat LLM comments as a gate before anyone has measured them.
- Blocking merges on day one. Engineers quickly learn to ignore a noisy required check.
- Skipping the data-path review. Security teams shut down tools that sent code somewhere nobody approved.
- No shared rules. Without your conventions written down, the bot enforces generic style.
- Losing the audit trail. Findings that live only in PR comments are hard to show an auditor.
How Origins AI Coding Tool runs code audit inside CI/CD
Origins AI (originshq.com) is a US-based AI-augmented engineering company that deploys self-hosted enterprise AI inside customers' environments. Its self-hosted AI coding assistant, the Origins AI Coding Tool, includes an AI code audit server: an example of the third platform type above.
According to the product page, the audit server runs as a CI/CD stage and checks each pull request for security vulnerabilities, logic errors, dependency risks and style violations. Custom skills encode your architecture decisions and security rules.
| What a security reviewer checks | What the product page lists |
|---|---|
| CI/CD systems | GitHub Actions, GitLab CI, Jenkins, CircleCI, custom webhooks |
| Output | SARIF, JSON, native GitHub and GitLab annotations |
| Models | Codex, Claude, your own model; Llama, Mistral or CodeLlama in air-gapped mode |
| Before the model | Secrets, API keys and PII redacted |
| Audit trail | Every LLM request, snippet and response logged in your environment |
On data location, the page states that in on-premise and air-gapped modes no source code is sent to any external service. In hybrid mode, code context sent to a hosted model leaves your network. The products overview says each product deploys on-premise or in your own cloud account, with an implementation team doing the rollout: a deployment, not a hosted subscription.
Talk to an engineer
If your security team needs code review to run inside your own network, book a call with an Origins AI engineer and bring the CI setup you use today.
Written by Apoorva Kumar, Co-Founder & CEO, Origins AI.