AI writes the code.
SonarQube analyzes and verifies it.
SonarQube provides engineering teams with an independent verification layer for AI-generated code, catching quality and security issues before they reach production. Trusted by 75% of the Fortune 100.
AI writes code faster than humans can review it
AI assistants now generate 30–50% of commits at many teams. They optimize for plausible-looking code — not secure, maintainable, or correct code.
Automatic verification on every AI-generated commit
SonarQube sits in your CI/CD pipeline and applies 7,000+ battle-tested rules to every pull request — before it ever reaches main.
Everything you need to ship AI code safely
Seven thousand rules. Forty languages. One platform that catches what your AI assistant misses.
Security analysis
OWASP Top 10, SANS Top 25, injection flaws, and AI-specific vulnerability patterns — caught before production.
AI code detection
Automatically identifies AI-generated contributions and applies deeper scrutiny — no manual labeling required.
AI CodeFix
Every finding comes with a ready-to-apply fix. Reduce MTTR from hours to seconds, automatically.
Quality gates
Custom pass/fail conditions block non-compliant code from merging — enforced on every pull request.
IDE integration
SonarLint brings analysis into VS Code and JetBrains — catch issues before you even open a PR.
Engineering dashboards
Track code health, debt trends, and AI code coverage across every team and repo in one unified view.
Catch what Copilot misses
AI assistants optimize for code that looks correct. SonarQube analyzes for what's actually true — using 16 years of real-world rule development across 40+ languages.
def get_user(user_id):
# BLOCKER: Command injection
cmd = f"grep {"{user_id}"} /etc/users"
return os.system(cmd)
# ✓ SonarQube AI CodeFix applied
def get_user(user_id: str):
validated = sanitize(user_id)
return db.users.get(id=validated)
Non-compliant code never reaches main
Define your own quality standards and let SonarQube enforce them on every pull request — human-written or AI-generated — automatically.
Start free. Scale with confidence.
From individual teams to enterprise — a plan that fits where you are today.
Essential for small teams.
Mission critical scale & performance.
Build trust into every line of code
Join 180,000+ developers who trust SonarQube to protect their codebase from AI-introduced risk. Free trial — no credit card required.
Common questions
What is AI code verification?
AI code verification is the process of checking AI-generated or AI-assisted code against defined standards for security, reliability, maintainability, and correctness before it is merged or released. It combines static code analysis, automated review, and quality gates so teams can verify code at the speed AI generates it.
How is AI code verification different from AI code review?
AI code review focuses on finding bugs, vulnerabilities, and maintainability issues in code changes. AI code verification goes further by confirming whether code meets organizational standards and release requirements through automated checks, policy enforcement, and quality gates.
What is AI code analysis?
AI code analysis is the automated inspection of AI-generated and human-written code to detect bugs, vulnerabilities, code smells, duplication, complexity, and maintainability risks. It helps teams understand whether AI-produced code is safe and production-ready.
What does AI code assurance mean?
AI code assurance means applying strict, repeatable controls to AI-generated code so teams can trust what gets merged and shipped. That includes automated analysis, pull request checks, policy enforcement, and verification workflows that reduce the risk of insecure or unreliable code reaching production.
Why is AI code verification important?
AI coding tools increase development speed, but they also increase review volume and can introduce defects, security gaps, and technical debt. Sonar research found that 96% of developers do not fully trust AI-generated code, which makes automated verification essential for safe adoption at scale.
Can static code analysis verify AI-generated code?
Yes. Static code analysis is one of the most effective ways to verify AI-generated code because it evaluates source code deterministically for quality and security issues without executing it. It can uncover vulnerabilities, bugs, correctness issues, and maintainability problems across both AI-generated and human-written code.
How do quality gates help verify AI-generated code?
Quality gates enforce required quality and security standards before code can move forward in the pipeline. For AI-generated code, they help teams block risky pull requests, require issues to be resolved, and make sure every change meets defined thresholds before merge or release.
How does AI PR review improve pull request workflows?
AI PR review brings automated analysis into pull request workflows so developers can catch bugs, security vulnerabilities, and maintainability issues earlier. This reduces manual review effort, speeds feedback loops, and helps teams review more AI-generated code with greater consistency.
How does AI CodeFix and remediation fit into code verification?
Code verification finds issues, while AI CodeFix and remediation help resolve them. After analysis detects bugs, vulnerabilities, or code smells, AI-assisted remediation can suggest targeted fixes so teams can move faster from detection to correction while keeping humans in control of final decisions.
How does AI code verification support AI governance and agentic development?
AI governance sets the policies and standards for how AI-generated code should be reviewed and approved. In agentic development workflows, verification enforces those standards with real-time feedback, automated checks, and auditable controls so both developers and agents operate within the same trusted framework.