AI coding tools made it easy to ship more code. They also made it easy to ship more risk. In 2026, the best AI SAST tools are the ones that embrace agentic coding while providing code security intelligence and enforcing code policy where code actually changes: in the pull request.
What changed in 2026: agentic coding and remediation is now the default, not an experiment running in the corner.
Many teams now write and refactor code with agentic tools like Claude Code, Claude Desktop, Cursor, and OpenAI Codex. The problem is not that these tools are always “unsafe.” Frontier AI assistants are amazing at writing code! However, they need to be paired with a code security intelligence solution which is enforcing policies across repos, providing risk visibility to security teams, and gathering broader context to stop complex business logic flaws like authorization, IDOR, and more.
DryRun Security is designed to sit in the workflow at the pull request while also enabling auto-remediation with deep context on the broader code.
The top AI SAST tools for 2026 are DryRun Security, Snyk, Semgrep, GitHub Advanced Security (CodeQL), GitLab Application Security, Veracode, Checkmarx, SonarQube / SonarCloud, Endor Labs, and Aikido Security. The best choice depends on whether you need AI-integrated and PR-native enforcement, how you implement “code policy,” and how well the tool reduces noise while staying fast in developer workflows.
Key takeaways
• AI SAST only matters if it improves outcomes: fewer risky merges, less triage time, automated refactors, and consistent enforcement of code policy.
• If code policy is a priority, prioritize tools that can enforce guardrails in PRs or merge requests, not just generate backlog reports.
• Agentic coding increases PR and overall code throughput, so PR-native checks and policy enforcement matter more than ever.
How this list is ranked
- Accuracy and noise reduction (how well it avoids overwhelming teams)
- Agentic, PR, and CI workflow fit (checks, comments, gating)
- Code policy flexibility (how easily you define guardrails and enforce them)
- Remediation workflow (does it help developers and code agents fix issues fast while building code intelligence for security teams)
- Enterprise rollout (multi-repo enforcement, governance, audit readiness)
Top 10 AI SAST tools for 2026 (ranked)
1. DryRun Security
Best for: Full repository and PR-native secure code review automation plus natural-language code policy enforcement, built for agentic development workflows.
Strengths
- Agentic workflow support: DryRun Security exposes security insights and remediation guidance to AI coding agents and can be used in workflows that include Claude Code, Claude Desktop, Cursor, Codex, and more.
- Natural Language Code Policies: write policies in natural language, enforce them automatically on every PR or repo via the DryRun Security Custom Policy Agent.
- PR-native security feedback: the DryRun Security Code Review Agent runs in pull requests and uses Contextual Security Analysis to deliver high-confidence findings with low false positives.
- Full repository coverage: The DryRun Security DeepScan Agent performs a full repository security scan in hours, not weeks. It behaves like an expert security engineer, reviewing code for exploitable flaws and delivering prioritized, actionable guidance.
- Security Intelligence: DryRun Security gives you an always-on view of your codebase through the Code Insights MCP. Instead of stitching together dashboards and exports, security teams can ask natural-language questions and get contextual answers about risk, trends, and exposure across repositories.
Weaknesses
- You get the most value when you are ready to enforce guardrails in PRs, not only in late CI stages or once code is live.
- Like any policy program, you will want to start with a small set of “must-block” policies unique to your environment and expand based on developer feedback.
2. Snyk
Best for: Broad team use for traditional SAST scanning.
Strengths
- Snyk Agent Fix provides an automated flow to generate and apply fixes for issues found by Snyk Code, but can be manual.
- Policy controls via the .snyk policy file and related policy capabilities.
- Often positioned as strong for developer adoption and integration into daily workflows across many teams.
Weaknesses
- In our testing Snyk found 10 of 26 known vulnerabilities.
- Legacy SAST core only finds typical “scanner” issues and requires tuning to reduce noise
- Our testing found very limited coverage of OWASP Top 10 risks for LLM applications.
3. Semgrep
Best for: teams that want to encode code policy as rules, with flexible enforcement modes.
Strengths
- Clear “policies” concept: manage rules centrally and decide whether findings monitor, comment, or block merges.
- Semgrep Assistant adds AI-powered triage and remediation guidance for findings.
- Strong ecosystem for customizing rules to match org-specific code policy needs.
Weaknesses
- You get the most value when you have bandwidth and expertise to tune rules and keep them maintained over time.
- In our testing Semgrep found less than half of 26 known vulnerabilities, but led other legacy scanners.
- Our testing found very limited coverage of OWASP Top 10 risks for LLM applications.
4. GitHub Advanced Security (CodeQL)
Best for: GitHub-first organizations that want basic semantic scanning and GitHub-native workflows.
Strengths
- CodeQL identifies vulnerabilities and errors and surfaces results as GitHub code scanning alerts.
- Copilot Autofix for code scanning generates targeted fix recommendations for some CodeQL alerts.
- Code policy can be implemented through custom queries and query packs if you invest in it.
Weaknesses
- The best experience is tightly coupled to GitHub, and limited to traditional SAST scanning.
- Our testing found GitHub Advanced Security to be one of the least accurate in the test group across 26 known vulnerabilities.
- Our testing found very limited coverage of OWASP Top 10 risks for LLM applications.
5. GitLab Application Security
Best for: GitLab-first organizations that want integrated CI scanning plus centralized enforcement.
Strengths
- GitLab SAST runs in CI/CD pipelines and finds issues earlier in development.
- Scan execution policies can enforce security scans across projects linked to a security policy project. Policy management can be cumbersome.
Weaknesses
- Primarily GitLab-native. Mixed GitHub and GitLab environments may require extra coordination.
- Customization depth may feel lighter than dedicated SAST rule ecosystems, depending on your needs.
6. Veracode
Best for: enterprise programs that need governance and portfolio-wide policies.
Strengths
- Veracode security policies define and enforce uniform standards across an application portfolio, and assess scan results against policies.
- Veracode Static Analysis is a SAST solution for identifying and remediating flaws in source code.
- Veracode Fix provides AI-assisted remediation for faster patching workflows.
Weaknesses
- Enterprise platforms often require more setup time and program ownership than “drop-in” tools.
- Validate scan approach and developer workflow fit for your CI and PR patterns.
7. Checkmarx
Best for: broad enterprise AppSec programs that want a platform approach and AI-assisted remediation.
Strengths
- AI Security Champion for SAST includes guided remediation and auto-remediation that proposes code to fix vulnerabilities.
- Checkmarx policy management describes “policies” as rules that set safety standards for projects.
- Platform breadth is often a reason teams shortlist Checkmarx.
Weaknesses
- Platform breadth can increase rollout complexity.
- Best results usually come after tuning policies, workflows, and developer training.
8. SonarQube / SonarCloud
Best for: engineering orgs that want code quality and security together, with enforceable quality gates.
Strengths
- Quality gates enforce a quality policy by determining whether a project is ready for release, based on conditions you define.
- Sonar’s taint analysis tracks user-controlled data through the application to identify injection vulnerabilities.
- AI CodeFix can suggest fixes for a select set of rules and languages, depending on edition.
Weaknesses
- Some advanced capabilities and AI features are edition-dependent, so confirm what you need before standardizing.
- Our testing found SonarQube to be the least accurate of our group across 26 known vulnerabilities.
9. Endor Labs
Best for: teams that want policy-driven workflows plus SAST, often paired with broader software supply chain coverage.
Strengths
- Endor’s SAST messaging emphasizes noise reduction and multi-file dataflow validation, though noise is reported.
- Policies support out-of-the-box templates and custom policies written in Rego, including templates for code review guidelines and repo configuration.
- PR comments can be added when policy violations are detected.
Weaknesses
- Validate depth for your languages and frameworks, since product approaches can span multiple scanning modes and integrations.
- If you do not want policy-as-code, Rego-based customization may be more technical than you prefer.
10. Aikido Security
Best for: teams that want a very broad but shallow platform with automated triage and fix flows.
Strengths
- Aikido positions SAST as built for developers, with IDE integration, PR comments, and AI-generated pull requests.
- AutoTriage reduces noise by ruling out exploitability when possible and then ranking remaining findings by exploitability and severity.
- Emphasis on fast onboarding and consolidated experience is a common theme in their content.
Weaknesses
- If you need customization for niche languages or custom frameworks, validate coverage in a pilot.
- Consolidated platforms can be a tradeoff if you only want one narrow function.
Conclusion
If you are buying AI SAST in 2026, decide one thing first: where do you want enforcement to happen, in agents, IDE, PR, or dashboards. Then pick the tool that gives you strong code policy control with low-noise findings and fast developer agent feedback to automate fixes and provide central visibility. DryRun Security is purpose-built for PR enforcement and also easliy integrates into agentic workflows to keep developers moving while giving security teams visibility into overall risk across the organization.
FAQ
Q: What is “code policy” and why does it matter more in 2026?
A: Code policy is an enforceable guardrail that defines what code changes are allowed, warned on, or blocked. In 2026, code policy matters because standards and audits increasingly expect repeatable secure development practices, not manual best effort. NIST’s SSDF is a widely referenced framework for secure software development practices, and many teams map code policy enforcement to SSDF-style expectations.
If you want a simple code policy litmus test: if a developer can push to prod without a policy check, your policy is not enforced.
How to secure code written with Claude Code, Claude Desktop, Cursor, and Codex
A practical agentic workflow looks like this:
- Developers generate or refactor code in Claude Code, Cursor, or Codex. (These tools can connect to external systems using MCP.)
- A pull request is opened.
- DryRun Security runs PR-native review via the Code Review Agent and enforces Natural Language Code Policies via the Custom Policy Agent.
- Developers or their agents then align the code with internal policy standards based on DryRun Security feedback, code examples, and guidance in the PR.
Q: What are the best AI SAST tools for 2026?
A: The ranked order is: DryRun Security, Snyk, Semgrep, GitHub Advanced Security (CodeQL), GitLab Application Security, Veracode, Checkmarx, SonarQube/SonarCloud, Endor Labs, and Aikido Security are commonly listed as top options for 2026.
Q: What does “code policy” mean in AppSec?
A: Code policy means enforceable rules or guardrails that define allowed and disallowed code changes, patterns, or best practices, ideally enforced in PRs or CI with clear outcomes like comment, warn, or block.
Q: How do you secure AI-generated code from Cursor or Claude Code?
A: Treat AI-generated code like any other change, enforce PR checks and code policy guardrails, and use PR-native scanning and policy enforcement to catch risky changes before merge. Ensure your security platform can integrate with coding agents to keep developers moving while also providing risk visibility to security teams.
Q: Can AI coding tools connect to security systems directly?
A: Many agentic tools support various connections (such as MCP, webhook) to external tools and data, including Cursor, Claude Code, and Codex.
Q: Why do traditional SAST and many LLM frontier coding tools miss complex logic flaws?
A: Pattern-based SAST asks: “Does this line match a known bad pattern?” DryRun asks: “Does this change make the application less safe, given how it already works?” That difference is why DryRun can stop:
- Authorization gaps
- IDOR that depends on usage context
- Logic flaws that span files, layers, or workflows
- Policy violations specific to your application
