Verify AI code, no matter who wrote it
SonarQube adds an independent verification layer that catches security and quality issues in AI-generated code before they reach production, no matter which assistant or agent wrote it.
- 4.6 on G2
- 7M+ developers use Sonar
- 75% of the Fortune 100
- Setup in 5 minutes
AI writes code faster than humans can review it
AI agents now write a large share of new code, and they optimize for output that looks plausible, not code that is secure, maintainable, or correct.
- Injection and cross-site scripting flaws introduced silently
- Hardcoded secrets and API keys committed undetected
- Technical debt piling up faster than any review can catch it
Automatic verification on every AI-generated commit
SonarQube analyzes every pull request in your pipeline, combining deterministic and reasoning-based checks to catch issues before code reaches main.
- Injection and cross-site scripting caught before merge
- Hardcoded secrets and keys flagged before they land in the repo
- Quality gates block risky code from ever merging
Everything you need to ship AI code safely
Seven thousand rules. Forty languages. One platform that catches what your AI assistant misses.
Reliability analysis
Detects bugs, code smells, and complexity in AI-generated code, surfacing maintainability risk before it ships.
Architecture
Reveals dependencies and architectural drift in AI-generated code so teams manage design before it compounds.
AI CodeFix
Every finding comes with a ready-to-apply fix, cutting time to remediation from hours to seconds.
Quality gates
Custom pass/fail conditions block non-compliant code from merging, enforced on every pull request.
IDE integration
Real-time analysis in your IDE flags bugs and vulnerabilities as you type, before code reaches the pull request.
CI/CD integration
Works with GitHub, GitLab, Azure DevOps, and Bitbucket, with zero changes to your existing pipeline.
Catch what AI assistants miss
AI assistants optimize for code that looks correct. Sonar analyzes what is actually true, combining deterministic and reasoning-based analysis across 40+ languages.
- Detects OWASP Top 10, SANS Top 25, and AI-specific vulnerability patterns before they reach production
- Catches hardcoded secrets, tokens, and API keys before they ever reach your repository
- Identifies AI-generated code automatically, with no manual labeling or extra configuration
Non-compliant code never reaches main
Define your quality and security standards, then let Sonar enforce them automatically on every pull request, whether the code was written by a human or an AI agent.
- Set custom conditions on coverage, open issues, duplication, and AI-generated code metrics
- Decorate pull requests in GitHub, GitLab, Azure DevOps, and Bitbucket where developers work
- Block non-compliant code from merging with zero changes to your existing pipeline
Start free. Scale with confidence.
From individual teams to enterprise — a plan that fits where you are today.
Team
Essential capabilities for small teams
- Recommended for teams <50 developers
- 30+ languages
- AI code detection
- Secrets detection
- AI-driven code fixes
- Pull request analysis
- Commercial support available
Enterprise
Mission critical scale & performance
- 40+ languages + IaC
- SAML SSO & audit logs
- OWASP, CWE, PCI DSS, and MISRA C++:2023
- Unlimited users and projects
- Enterprise hierarchy, portfolios, org-wide defaults
- Enterprise SLA
- Premium support available
Frequently asked questions
Can Sonar verify code from any AI assistant?
Sonar verifies code independently of the tool that generated it, so output from Copilot, Cursor, Claude Code, or any other assistant is held to the same quality and security standards. Because verification runs in the IDE and CI pipeline rather than inside the generating tool, teams get a consistent bar across every AI assistant and repository.
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.
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 CodeFix 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?
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.
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.
What is AI code assurance?
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.