AI code security has become a two-sided problem. Coding assistants now write a large share of new code, and the same AI wave has changed how scanners find and fix flaws in it. Static analysis (SAST) and software composition analysis (SCA) tools no longer stop at a list of alerts. The better ones now triage findings, explain them in plain language and open a pull request with a proposed fix.
That matters because volume is the real enemy. A scanner that flags 4,000 issues and leaves engineers to sort them is barely better than no scanner. The tools on this list use AI to cut that noise, find logic flaws that rule-based engines miss, and shorten the path from alert to merged fix.
If your team ships a lot of AI-generated code, read our breakdown of vibe coding security risks first, then use this list to pick the scanner that fits your stack. Every product detail below comes from the vendor’s own site or documentation, checked in November 2026.
AI Code Security Scanning Tools At A Glance
Company
Based In
Best For
Known For
Snyk
Boston, USA
Developer-first SAST and SCA
DeepCode AI and Snyk Agent Fix
GitHub Advanced Security
San Francisco, USA
Teams already on GitHub
CodeQL with Copilot Autofix
Semgrep
San Francisco, USA
Custom rules plus AI triage
Semgrep Multimodal autotriage and autofix
Checkmarx One
Atlanta, USA
Large enterprise AppSec programs
Fusion SAST and Assist agents
Veracode
Burlington, USA
Regulated enterprises
Veracode Fix remediation
Aikido Security
Ghent, Belgium
Startups and scale-ups wanting one platform
All-in-one scanning with AI AutoFix
Endor Labs
Palo Alto, USA
Reducing open source dependency noise
Reachability analysis and AI SAST
ZeroPath
San Francisco, USA
Finding business logic and auth flaws
AI-native SAST with verified patches
Corgea
San Francisco Bay Area, USA
Teams that want fixes applied automatically
AI-native SAST with high-confidence autofix
DryRun Security
Austin, USA
Pull request security review
Context-aware adversarial PR reviews
Best AI Code Security Scanning Tools For 2027
1. Snyk — Hybrid DeepCode AI Engine With One-Click Agent Fix
Snyk is one of the best-known developer security platforms, founded in 2015 and headquartered in Boston. Snyk Code, its SAST product, scans in the IDE and on pull requests and gives in-line remediation advice where developers already work. It sits alongside Snyk Open Source for dependencies, plus container and infrastructure as code scanning.
The engine underneath is DeepCode AI, which Snyk describes as a hybrid of symbolic AI, generative AI and several machine learning methods, guided by its own security researchers. Snyk says the fix model was trained on millions of permissively licensed open source projects with verified fixes, and never on customer data. It supports 19 or more languages. Snyk Agent Fix turns findings into suggested patches that can be applied with one click.
There is a free plan with 5 projects and 100 Snyk Code tests per month, and a Team plan starting at $25/month as listed in November 2026, with 1,000 Snyk Code tests per month. Enterprise pricing is by quote on a credit model. The trade-off is that test caps on the lower tiers can bite quickly for busy repos, so check your scan volume before committing.
For teams that already host code on GitHub, GitHub Code Security is the lowest-friction option. It runs CodeQL code scanning and adds Copilot Autofix, which feeds the alert, the surrounding code and the query help text to a large language model and returns a proposed change plus a plain-language explanation. Fixes show up on pull requests and on the default branch.
According to GitHub’s documentation, Copilot Autofix covers a subset of CodeQL queries for C#, C/C++, Go, Java/Kotlin, Swift, JavaScript/TypeScript, Python, Ruby and Rust, and it does not need a separate Copilot subscription. GitHub lists Code Security at $30 per active committer per month and Secret Protection at $19 per active committer per month as of November 2026.
GitHub is unusually candid about limits. Its docs warn that suggestions are non-deterministic, can struggle with multi-file data flow, may be skipped for very large files, and can occasionally introduce new issues. That makes human review of every autofix essential, and it means teams on GitLab or Bitbucket will want a different tool.
3. Semgrep — Rule-Based SAST Paired With AI Autotriage And Memories
Semgrep, based in San Francisco, built its name on a fast, readable rule engine that security teams can extend with their own patterns. Its AI layer, now documented as Semgrep Multimodal (the successor to Semgrep Assistant), adds autotriage that recommends whether a finding can be ignored and explains why, autofix that opens pull or merge requests, and a beta noise filter that suppresses PR comments for likely false positives.
A feature called Memories lets teams tune remediation guidance per project and per rule, so the AI learns house standards over time. Semgrep says combining deterministic rules with AI analysis finds more true positives than AI alone, and that it targets complex flaws such as IDOR and business logic bugs that classic pattern matching misses.
Pricing is transparent. The free edition covers up to 10 contributors and 10 repositories with 60 AI credits, and the Teams plan starts at $30 per contributor per month for Code (SAST), as listed in November 2026, with Supply Chain and Secrets priced as add-ons. Costs can stack up if you buy all three modules for a large team.
4. Checkmarx One — Enterprise AppSec Platform With Developer, Triage And Remediation Assist
Checkmarx, founded in 2006 and headquartered in Atlanta, is a long-standing name in enterprise SAST. Checkmarx One bundles code security (Fusion SAST, secrets detection, IaC and API security), supply chain security (SCA, malicious package protection, container security) and application security posture management that correlates findings and ranks them by exploitability.
Its AI features come as three assistants. Developer Assist runs security checks inside VS Code, JetBrains, Visual Studio and Eclipse. Triage Assist ranks findings by attackability, and Remediation Assist generates fixes intended to be merge-ready. Checkmarx also offers an MCP server for agent access and an AI supply chain module with an AI-BOM and LLM scanner.
Checkmarx states that its automated fixes are recommendations that customers must review, test and approve. The platform suits large security teams with many applications and compliance needs. Smaller teams may find it heavier to roll out than developer-first tools, and pricing is not published.
5. Veracode — Veracode Fix For First-Party Code And Open Source Dependencies
Veracode, founded in 2006 and based in Burlington, Massachusetts, is a common choice for regulated industries that need mature reporting and policy controls. Its AI remediation product, Veracode Fix, handles both first-party code and open source dependencies, which sets it apart from tools that only patch one or the other.
Veracode Fix delivers fix guidance and evidence-rich pull requests in the IDE, CLI and CI/CD pipeline, with built-in quality gates. Veracode says the architecture does not retain customer code, and it runs contextual impact analysis to prioritize safe, compatible dependency updates rather than blindly bumping versions.
The trade-off is that Veracode is priced and sold as an enterprise platform, with no public price list. Teams that want a quick self-serve start will usually look at Snyk, Semgrep or Aikido first.
6. Aikido Security — All-In-One Code And Cloud Scanner With AI AutoFix And Flat Team Pricing
Aikido Security, with offices in Ghent, Chicago and London, packs a wide range of scanners into one product: SAST, dependency scanning, secrets detection, license and SBOM checks, IaC, container images, cloud posture, DAST and API scanning. On top sit AI code analysis and AI AutoFix, which proposes patches for findings across those scanners.
Aikido claims more than 150,000 organizations use it and shows customers including Revolut, Deel, n8n, Lovable, SoundCloud and Toyota. That breadth appeals to startups and scale-ups that do not have a dedicated security team to stitch together five tools.
Pricing is flat by team rather than per developer. As listed in November 2026, the Developer plan is free for 2 users and 10 repos, Basic is $300/month including 10 users and 100 repos, and Pro is $600/month including 10 users and 200 repos. The catch with any all-in-one tool is depth: specialists may go further on a single area such as reachability or PR review.
7. Endor Labs — Reachability-Based SCA Plus Agentic AI Security Code Review
Endor Labs, founded in 2021 and based in Palo Alto, started by attacking the noisiest corner of AppSec: open source vulnerabilities that your code never actually calls. Its reachability analysis only adds a vulnerability to the backlog when your code can reach it. The company cites a 97% noise reduction at Zebra Technologies.
It has since added AI SAST, where agents trace data flow across repositories and pull requests, and AI security code review, where developer, architect and security engineer agents review every PR for architectural risks. Examples Endor gives include endpoints shipped without authentication, removed OAuth state parameters and new PII collection.
Published customers include Egnyte, Peloton, Netskope and Five9, with testimonials from security leaders at Glean, Rubrik and Atlassian. Pricing is not public, and the product is aimed at mid-size and larger engineering organizations rather than solo developers.
8. ZeroPath — AI-Native SAST That Finds Business Logic And Auth Bypass Flaws
ZeroPath, founded in 2024 in San Francisco, is one of the newer AI-native SAST vendors and was named a Top 10 finalist in the RSAC 2026 Innovation Sandbox. It focuses on the class of bugs rule-based scanners struggle with most: business logic flaws, authentication bypasses and broken authorization, then tries to confirm exploitability before reporting.
Setup is light, with zero-config scanning that does not need build scripts. Its SAST Autofix generates patches meant to compile, pass tests and merge cleanly. Beyond SAST, it lists SCA, secrets, IaC, PR reviews, DAST, container scanning and an AI-BOM. It connects to GitHub, GitLab, Bitbucket, Azure DevOps and Gerrit, plus Jira, Linear and Slack.
Named users include Aptos Labs and Exodus, and curl maintainer Daniel Stenberg has spoken publicly about it. As a young company, ZeroPath has a shorter track record than incumbents, and pricing requires a demo.
9. Corgea — AI-Native SAST With High-Confidence Auto-Applied Fixes
Corgea, a Y Combinator-backed company in the San Francisco Bay Area, finds, triages and fixes vulnerabilities across code, packages, infrastructure and containers. Its AI SAST is paired with automatic fixes that can be applied without manual steps when confidence is high, which suits teams with long security backlogs.
Integrations cover GitHub, GitLab, Azure DevOps, Bitbucket and Harness, and IDEs including VS Code, Cursor, Visual Studio 2022 and IntelliJ. Corgea also works with coding agents such as Claude Code, Copilot and Codex, which helps if your team already uses one of the tools in our Claude Code vs Cursor comparison. Named customers include Zapier and epilot, and the company holds SOC 2 Type II.
There is a free start option and a 14-day trial of the Growth tier with no credit card. Corgea does not list which languages it supports on its homepage, so confirm coverage for your stack during the trial.
10. DryRun Security — Context-Aware Adversarial Review On Every Pull Request
DryRun Security, based in Austin, Texas, takes a different angle from classic scanners. Instead of matching patterns, it builds a security context graph of your application (routes, authentication, authorization, access controls and data flows) and runs an adversarial review on each pull request before it merges.
That context lets it catch issues such as a missing tenant check on an invoice lookup, prompt injection or unsanitized LLM output. Teams can write security policies in natural language and choose which severities block a merge. DryRun also offers full-repository DeepScan reviews and dependency exploit validation, which it says cut one customer’s 4,000 dependency findings to 397.
It works with GitHub and GitLab and exposes an MCP server for coding agents like Cursor and Claude Code. Named customers include Tines and BrightHR. DryRun is strongest as a PR gate, so teams that need broad compliance reporting may pair it with a platform scanner.
How To Choose An AI Code Security Scanning Tool
Do You Need SAST, SCA Or Both?
SAST looks for flaws in code you write. SCA looks for known vulnerabilities in the open source packages you pull in. Most breaches touch both, so platforms such as Snyk, Aikido and Checkmarx One cover both in one place. If dependency noise is your biggest pain, a reachability-focused tool like Endor Labs is worth a close look.
Do You Need To Catch Logic Flaws, Not Just Known Patterns?
Rule-based engines are excellent at SQL injection and cross-site scripting but weak at broken authorization and business logic bugs. AI-native tools such as ZeroPath, DryRun Security and Endor Labs’ AI SAST are built to reason about how your application behaves. This matters more as AI assistants generate code that compiles but skips an access check. Our guide to the best vibe coding tools covers where those gaps tend to appear.
Do You Trust Autofix Enough To Merge It?
Every vendor here offers some form of AI-generated fix, and every serious one says a human should review it. Look at how fixes are validated (tests, compile checks, quality gates) and whether you can set confidence thresholds. Treat autofix as a fast first draft, not an approval.
Does It Fit Your Code Host And IDEs?
GitHub Advanced Security only makes sense on GitHub. Others support GitLab, Bitbucket and Azure DevOps to varying degrees. Check IDE plugins and MCP support too, since developers fix issues faster when the scanner speaks to them inside the editor or the coding agent they already use.
How Does Pricing Scale With Your Team?
Pricing models differ widely. GitHub and Semgrep charge per committer or contributor, Snyk caps tests per tier, and Aikido uses flat team bundles. Model your cost at today’s headcount and at double it, and ask about free tiers so you can run a real pilot on your own repositories first.
Conclusion
The best AI code security tool is the one your developers will actually act on. For broad, developer-first coverage, Snyk, Semgrep and Aikido are strong starting points. Teams deep in GitHub get a lot from Copilot Autofix, while large enterprises will compare Checkmarx One and Veracode. If logic flaws and PR-level review are your priority, ZeroPath, DryRun Security, Corgea and Endor Labs push AI furthest.
Whatever you choose, run a pilot on real repositories, measure how many findings are true positives, and track how many autofixes merge without rework. Those two numbers will tell you more than any vendor benchmark.
