AI SAST tools use AI to analyze code the way a security engineer would, catching business logic flaws, broken access control, multi-step exploit chains, and vulnerabilities that pattern-based scanners structurally miss. Some use AI instead of the rule engine entirely, while others layer AI on top of a deterministic scanner. A few do both as separate products, since the two approaches have different use cases and benefits.
This roundup covers 10 tools that bring AI into static code analysis. Tools are grouped by how AI participates in detection: AI-native tools where a reasoning model does the finding, AI-assisted tools where AI extends or assists a deterministic engine, and one platform that offers both.
TL;DR
Aikido is one of only two tools on this list that ships both a deterministic AI-assisted SAST scanner and a separate AI-native detection product (AI Code Audit) that independently reasons about business logic, access control, and exploit chains. Most tools only offer one of these. AI Code Audit provides AI pentest-grade detection at a lower price. Many of the incumbents give you deterministic detection with AI added on later for triage or language extension. Aikido gives you both as distinct products on one platform.
What is AI SAST?
Not every tool marketed as "AI SAST" uses AI the same way.
AI-native SAST tools use a reasoning model as the primary detection engine. There’s no rule engine underneath, like traditional SAST. Examples of vendors that offer modern AI-native SAST include Aikido Security, BlackDuck, ZeroPath, Corgea, AISLE, and Depthfirst. The AI reads the code and finds vulnerabilities, as a senior engineer might. As a result, AI-native SAST can detect more complex vulnerabilities, including:
- Business-logic vulnerabilities
- Broken authentication
- Missing authorization checks
- Race conditions
- Timing attacks
- IDORs
AI-assisted SAST tools keep a deterministic engine for detection and add AI for triage, coverage extension to unsupported languages, or remediation. Because AI doesn’t read the whole codebase, results are available faster. While it generally doesn’t find as many vulnerabilities as AI-native SAST, it is much cheaper and consequently more scalable. Examples on this list include Aikido Security, Black Duck, Checkmarx, Snyk Code, GitHub Advanced Security, and Endor Labs.
AI-native SAST and deterministic SAST tools have different roles in the security stack. AI in triage reduces noise, while AI in detection expands coverage to vulnerability classes that rules can’t express. Having both means you get reproducible, rule-based scanning on every commit and AI-powered reasoning on demand, without stitching together two vendors.
Read our blog post on AI SAST to learn about how it works and the different types of AI SAST.
Picking the right AI-SAST tool for your team
When evaluating AI SAST tools, first, you need to decide on your case
Do you have a SAST tool, but want to incorporate AI for better triage and auto-fix? The AI-assisted tools provide a good starting point. But if you want to go beyond traditional SAST and have an AI find business logic and access control vulnerabilities, you’ll likely want AI-native SAST. If you want both, do you want them integrated all into one platform, or can you accept having additional vendors in your security stack?
Next, you have to cut through the noise and figure out what each vendor’s AI SAST tool actually offers. When describing their AI SAST product, how much of the AI is in detection versus triage and remediation? Detection signal AI-native SAST, while triage and remediation alone mean it’s AI-assisted SAST. Our top 10 list below clearly outlines what type of AI features each company offers.
Once you decide on what type of tool you need, you can narrow down based on the features:
- Does the tool fit your SCM and CI/CD setup?
- How does pricing scale with your team size?
- What's the false-positive rate on your actual codebase, not on the vendor's demo app?
Top AI SAST tools comparison
Dual AI-native SAST and AI-assisted SAST tools
Platforms that offer separate AI-native SAST tools and AI-assisted SAST, offering deterministic testing alongside newer AI review.
1. Aikido Security
Aikido Security is a rare security platform that offers AI-native SAST and AI-assisted traditional SAST, covering teams with different workflow needs.
AI Code Analysis provides pentest-grade security reasoning applied directly to source code. But unlike AI pentesting, you don’t need a staging URL or live environment. To set it up, you just need to give it repo access. Multiple security agents work through your codebase together, chaining context across files and modules to surface the kinds of issues that pattern-based scanners structurally can't see.
AI agents reason about intent, following references across files, trace data flow across service boundaries, and analyze monorepos with multiple services or packages end-to-end. The full coverage list spans access control (IDORs/BOLA, privilege escalation), injection flaws (SQL, NoSQL, command injection, RCE, SSTI), XSS/CSRF/open redirects, authentication and session logic, SSRF/deserialization/file handling, cryptographic failures, and business logic flaws.
AI Code Analysis also reaches code paths that you can’t safely run a live pentest against. AI Code Audit surfaces around up to 80 percent of the issues a human pentest would find, at roughly a tenth of the cost. It flags DoS and ReDoS patterns from the source code without crashing a live app, and can review feature-flagged code that hasn't been released publicly yet. And it works across all languages with no limitations, including legacy and niche ones like Fortran, COBOL, VB6, RPG, Gleam, and Solidity. Coverage extends beyond web apps to mobile apps, smart contracts, desktop apps, and IaC.
Aikido also ships a deterministic SAST scanner. It’s been integrated with AI since day one, with AI-powered noise reduction, designed to run on every commit. Aikido’s LLMs reduce noise and false positives compared to other SAST tools by over 90%. When the SAST tool finds a vulnerability and a fix is available, AI AutoFix attaches the suggested diff to the comment so you can review and commit it at once, or opens a dedicated PR whose metadata you can fine-tune to match your contribution guidelines. You can adjust any generated fix with natural-language follow-ups so it matches your standards, and write custom code checks in plain English instead of regex.
Aikido is built with a developer-first philosophy, so findings show up where the developers work. The IDE plugins for VS Code and other editors scan on file open and save, highlight issues inline, and list them in an Aikido panel. Hovering a finding lets you assess its impact with AutoTriage, and AutoFix shows a side-by-side preview you apply in place, after which the file is rescanned to confirm the issue is resolved. Everything routes into tools teams already use. Aikido connects to Jira Cloud, Linear, Slack, Bitbucket, and Azure DevOps, and can insert Jira ticket IDs into AutoFix PR titles for traceability between fixes and tickets.
For teams that want runtime validation too, Aikido Pentest runs the same agentic engine against a live target as AI Code Audit, and the two products complement each other. Use Code Audit for source-level reasoning on demand and Pentest for live exploitability proof when you have a running test environment.
Best for: Teams that want pentest-depth code reasoning without environment setup, on-demand, and at a fraction of pentest cost, with findings that cover business logic and access control flaws across any language.
Check out Aikido’s full static code review stack:
2. Black Duck
Black Duck is the other platform on this list that ships both AI-native SAST and AI-assisted SAST.
Black Duck Signal is the AI-native scanner, available since March 2026. Like Aikido’s AI Code Audit, it uses an agentic architecture (multiple role-based and task-based agents rather than a single model) powered by ContextAI, Black Duck's security model. Signal runs independently and feeds its findings into its Polaris platform, where it can also add reachability analysis on top of the deterministic SAST findings to prioritize what's actually exploitable.
Black Duck's deterministic SAST is sold as either Coverity (on-prem) or as the Polaris Platform (SaaS), where the SAST capability is delivered as Polaris fAST Static. The AI assistance on top of their deterministic SAST comes from Black Duck Assist, integrated into both the Polaris platform and the Code Sight IDE plugin. Assist generates plain-language summaries of SAST findings, suggests code fixes developers can paste into their work, and lets developers ask natural-language questions about their scan results. It works across VS Code, Visual Studio, IntelliJ, Eclipse, Cursor, and Windsurf.
However, while BlackDuck is one of the few companies with AI-native SAST and AI-assisted SAST, and the two products are stitched together at the reporting layer rather than natively unified, since Signal surfaces in Polaris through External Analysis rather than running as one engine. Coverity itself is slow, and many teams avoid running it on every commit because of scan time. So, as a deterministic SAST tool, it doesn’t work for fast-moving teams that also want every commit secured.
Neither product has public pricing, so you are stuck in an enterprise sales-led timeline, and it’s priced accordingly. The broader platform spans Polaris, Coverity, Signal, SCA, DAST, IAST, fuzzing, and ASPM, which is heavy to deploy and manage (and make sense of all their offerings). The platform requires dedicated infrastructure and admin effort because it’s built for centralized enterprise security teams rather than individual developers.
Best for: Large enterprises in regulated industries that want a deterministic engine with an emphasis on compliance alongside a separate AI-native scanner, and have the budget and staff for an enterprise platform.
AI-native SAST tools
AI-native SAST tools use an AI to read code to find vulnerabilities. These tools use a reasoning model as the primary detection engine. Instead of pattern matching, the AI does the finding.
3. ZeroPath
Founded by ex-Tesla Red Team and ex-Google Security engineers, ZeroPath combines LLMs with program analysis to scan pull requests for vulnerabilities.
ZeroPath lets you steer detection in plain English. Natural-language policies are org-specific rules, such as "flag any API endpoint that doesn't check user permissions." The same mechanism narrows noise in reverse. You can tell the scanner that certain inputs are sanitized upstream or that broken-auth issues are out of scope, and it factors that in on the next scan.
ZeroPath also puts results in the developers’ workflow, posting as comments in PRs. For remediation, ZeroPath generates a patch, validates it, resolves the issue without breaking existing behavior, and lets you apply it with a single click, opening a PR with the fix. You can reshape a generated fix with natural-language commands like "make this async-safe". The "Zero" assistant answers questions about any finding, such as why it's vulnerable or how it could be exploited, and an open-source MCP server surfaces findings inside Claude, Cursor, and Windsurf.
PR scanning is diff-only, meaning it scans changed lines rather than the full codebase on each pull request. If a vulnerability exists in unchanged code that a PR now makes reachable, the PR scan may miss it. Language coverage sits around 16 languages, which is narrower than that of 30 or more that some competitors offer. The company is young without the enterprise scale and support maturity of larger vendors.
ZeroPath has also recently shipped DAST and runtime-validation capabilities, but doesn’t cover deterministic SAST.
Best for: Developer teams that want only AI-native PR scanning with minimal setup and are comfortable with a younger, fast-moving vendor.
4. Corgea
Corgea is another AI-native SAST tool. Its CodeIQ engine pairs LLM reasoning with AST parsing for detection. PolicyIQ lets teams define business context in natural language, and the company has fine-tuned its own LLM for enterprise and private-cloud deployments. Reachability analysis traces runtime paths from public entry points to exploitable code, so teams can prioritize what attackers can actually reach. Customers include Zapier, epilot, Sonae, YAGEO, and First Resonance.
When it comes to performance, an independent pentester review confirmed that Corgea finds real vulnerabilities but ranked behind ZeroPath for that specific dataset. The same review found that in polyglot or monorepo setups, Corgea tries to identify and focus on the dominant application and silently ignores the rest of the code. If you have a Python API and a TypeScript frontend in the same repo, it may only scan one. No details are published on training data, evaluation methodology, or model architecture for the fine-tuned LLM.
Corgea covers over 20 languages, but fewer than the other tools, especially on the enterprise side. However, Corgea is more affordable than some alternatives. Pricing starts with a permanent free tier for individual developers, with paid plans at 39 and 49 dollars per developer per month, and custom enterprise pricing.
Best for: smaller teams and individual developers who want to try AI-native SAST without a sales process, and who work primarily in single-language repositories.
5. AISLE
AISLE is another AI-native SAST tool with reasoning agents that cross-check each other before surfacing a finding. Built AI-native from the ground up, there are no static rules underneath. It claims to work on any language and any codebase. As a newer player, AISLE has worked to establish its credibility by scanning a couple dozen open-source projects, including Firefox, and publishing the CVEs it's found.
While the CVE findings are promising, there is no public documentation, so you can't see how the product works until you are in a demo. All proof is concentrated in hardened open-source C/C++ libraries, which is great for C++ codebases but says less about how it performs in a shop that uses Java or interpreted languages.
Access is enterprise-only through a demo and a two-week proof of value, with no self-serve option and no public pricing. AISLE does not have a deterministic SAST tool, which you’ll want for consistent results and CI gating.
Best for: Organizations securing large C/C++ codebases or critical infrastructure projects where catching novel, deep vulnerabilities matters more than rule-based reproducibility.
6. Depthfirst
Depthfirst offers a different take on AI SAST with its Code product. Code runs a four-stage agentic lifecycle: finds vulnerabilities, then validates, then fixes, then verifies. The unusual step is the validation stage, which runs a dynamic test against a running instance of your application before surfacing a finding.
The fix stage automatically generates a pull request for every confirmed vulnerability, with code changes written to match your codebase's conventions. The verify stage replays the original attack against the running application after the fix is merged, and Depthfirst only marks a vulnerability resolved when exploitation actually fails on the live app, not just when the code changes. The flow, combining SAST detection with pentest validation and post-merge re-attack, puts Depthfirst closer to AI SAST fused with AI pentesting than pure source-only analysis.
Because the validation stage runs a dynamic test, you need a deployable environment for the full value. Because the setup (and potential cost) overhead pushes the tool into pentest territory, Depthfirst may not be a fit for teams looking for source-only scanning. If you do want AI agents running against your live environment, you may want to compare against other AI pentest tools.
As far as cost, there’s no transparent self-serve pricing. There’s no public documentation nor a free tier, so the only entry path is a demo request if you want to learn more or try it out.
Best for: Teams that want exploit-validated findings and are willing to provide a running application environment alongside source code access.
AI-assisted SAST tools
These tools keep a deterministic engine for detection and add AI for triage, coverage extension to unsupported languages, or remediation. A pattern-matching engine uses rules to find the vulnerabilities, while the AI generally supports triage, false-positive reduction, and auto-fix features.
7. Checkmarx
Checkmarx scans code for organizations across regulated industries, and its SAST, with taint analysis and data-flow tracking across multi-layer applications, is a long-time tool in the space. Checkmarx's core detection runs on CxQL (Checkmarx Query Language), its proprietary language for writing SAST detection rules.
Checkmarx launched AI SAST in March 2026 as a hybrid engine combining LLMs with its existing query-based analysis. The AI layer focuses on extending detection to languages where Checkmarx doesn't have CxQL queries written. For languages that Checkmarx already supports, detection still uses the same CxQL engine it has always used. Checkmarx tends to have good language coverage, especially for legacy systems.
When it comes to its AI features, Checkmarx’s agentic features are triage and remediation, not independent detection. Alongside AI SAST, Checkmarx shipped agentic agents, which include Triage Assist, Remediation Assist, and Developer Assist, AI Supply Chain Security, and DAST for AI.
Checkmarx, built during the waterfall era, tends to be a slower product. Full SAST scans take 25 to 45 minutes per application, which is orders of magnitude slower than other deterministic SAST tools that return results in seconds. It also requires a team to manage it and is less dev-focused than other options in the market.
Implementing Checkmarx in an organization can often take six weeks or more, and pricing is opaque and quote-based through sales, starting at around 40,000 dollars annually.
Best for: Large, security-mature enterprises in regulated industries that already have AppSec engineering staff and want to extend an existing Checkmarx investment with AI-powered detection for newer languages.
8. Snyk Code
Snyk Code is the SAST product within the broader Snyk AppSec platform. Snyk's broader AI push in 2026, which includes AI Security Fabric, Agent Scan, and Agent Guard, is focused more on securing AI-generated code and AI agents, not on expanding what SAST detection can find. However, there Snyk does have some AI-assisted SAST features.
Snyk's autofix is now called Snyk Agent Fix, rebranded from DeepCode AI Fix in May 2026 and rebuilt with an agentic architecture. When the detection engine finds a vulnerability, Snyk pulls human-written fix examples for that CWE from a database of thousands of handwritten pairs, feeds them to an LLM as few-shot prompts, generates candidate fixes, and re-runs static analysis on each one to verify the fix actually worked. If verification fails, the error feeds back to the model, and it retries. Agent Fix is limited to local-file fixes and doesn't handle vulnerabilities that span multiple files.
Snyk’s SAST engine uses more traditional machine learning, instead of an LLM, for noise reduction, specifically when it comes to well-known vulnerability classes like command injection and hardcoded secrets. If you want an LLM involved in code triage and prioritization, you’ll have to go with a different SAST vendor. Snyk's ML, trained on millions of open-source data-flow examples, learns to recognize patterns about how information moves through an application, so the engine that traces data flowing through function calls. However, Snyk is known to still produce noisier SAST findings than its competitors.
Pricing scales per contributing developer with a cliff into Enterprise at 15,000 dollars or more per year past 10 developers. The engine is closed with no custom rules, which may not work for teams that want to customize their deterministic SAST tool. Language support from the docs is 12 languages (Apex, C, C++, C#, Go, Java, JavaScript, PHP, Python, Ruby, Swift, and TypeScript), although Snyk claims support for 19. Snyk started as an SCA tool, and SAST came later.
Best for: Developer-first teams that value a mature, scanning experience integrated across IDE, CLI, and CI/CD, that can absorb the per-developer pricing model and don’t need AI-native SAST.
9. GitHub Advanced Security
GitHub Advanced Security pairs its SAST engine, powered by GitHub’s own CodeQL, with newer AI-powered security detections.
One of the biggest advantages of GitHub Advanced Security (GHAS) is that it integrates directly into GitHub and Azure DevOps repositories, so teams already on those platforms don't need to adopt a separate tool.
GitHub’s Copilot code review is one of GitHub's AI security surfaces. You add Copilot as a reviewer on a pull request, and it posts inline comments on potential bugs, security issues, performance problems, and code quality concerns, with suggested changes you can apply in a couple of clicks. It reads the full diff across changed files plus the surrounding file content, not only the modified lines, so its feedback accounts for how a change fits the code around it. It also drafts PR summaries that list affected files and call out what a reviewer should focus on. You can run it on GitHub.com, inside the IDE, and from the GitHub CLI with a /review command.
Copilot code’s scope is more limited than comparable products, however. Copilot code review handles a single pull request within one repository well, but multi-repo architectures and large monorepos hit context boundaries. More specifically, it has limited awareness of cross-repository dependencies or architectural drift. On cost, code review runs through Copilot rather than GHAS, though an admin can enable it for contributors without a full Copilot seat by turning on two org policies.
Copilot Autofix is GitHub's other primary AI security feature, included in GHAS at no extra Copilot cost. It takes a CodeQL alert's description and location to generate both a suggested code fix and a plain-language explanation, surfaced on pull requests and the default branch. One limitation, however, is that Autofix only suggests a fix for well-understood patterns. If the code is complex or a fix might break something, GitHub surfaces the alert but offers no suggestion, so you need to patch it manually. Fix generation supported for C#, C/C++, Go, Java/Kotlin, Swift, JavaScript/TypeScript, Python, Ruby, and Rust.
The product is pretty prescriptive with the stack. Great if you like the GitHub and Microsoft ecosystem, but probably a con if you don’t. There is no option to use a different AI layer, so you’re stuck with Copilot, whether you like Microsoft’s AI assistant or not. With the platform bound to GitHub, teams on GitLab or Bitbucket won’t be able to use it. CodeQL custom queries also have a learning curve for the unacquainted.
AI-powered detections, which would likely be an AI-native SAST offering, were planned for public preview in Q2 2026, but availability is unconfirmed at the time of writing. It will extend coverage to ecosystems where CodeQL has no queries, including Dockerfiles and PHP.
Best for: Teams already on GitHub or Azure DevOps that want AI-augmented security scanning embedded in their existing pull request workflow.
10. Endor Labs
Endor Labs AI SAST tool, released in 2025, is a part of the Endor Code product. Their SAST system runs Opengrep for detection, and if the AI SAST is enabled, it then passes findings through AI agents for classification.
According to their docs, the AI SAST feature can be easily turned on with a CLI flag, which allows their AI agent to automatically classify findings as true positives or false positives. Additionally, Endor Labs’ AI Security Review tool passes the results from deterministic SAST to an AI, which takes those findings and writes a PR-friendly security analysis with severity levels and explanations.
While they have a few different AI features, some of them are not clear. Endor Labs says that they use Detection agents that find IDORs, but the implementation outlined in the docs doesn't support this. Endor outlines a “Code API” that powers detection agents, but no public Code API documentation exists to verify what it does or how it works. If you’re considering Endor Labs, make sure to clarify what type of detection agents they’ll provide.
As a platform, Endor Labs has good SCA, secrets scanning, and basic container image scanning. Endor Labs is SCA first, so the standalone SAST is the newer motion. This may be a good option for teams with a strong emphasis on SCA.
Endor Labs doesn’t have AI-native SAST or AI pentesting, and there is no free self-serve tier.
Best for: Enterprises already using or evaluating Endor Labs for SCA who want AI-powered triage and noise reduction on top of Opengrep-based SAST findings.
Bonus: AI code review agents with security scanning
A separate class of tools adds AI SAST features into code quality review. These are AI code review agents whose main job is reviewing pull requests for quality, logic, and team standards, with security checks bundled in as one layer.
Panto AI
Panto is an AI code review agent that happens to bundle security scanning. Panto analyzes PRs and gives line-by-line feedback on quality, logic, and security across 30-plus languages. Its design runs on three layers. A business context layer pulls metadata from Jira, Confluence, and design docs to align each PR with its purpose, a quality and security layer runs the static checks, and the model reasons about why the code was written, not only what it does. That business-context alignment, which Panto calls its "AI OS," is the feature that sets it apart from a pure scanner. Panto adds a layer of surveillance with a team visibility dashboard showing engineering managers what's slowing reviews and who's overloaded.
Panto routes inference through OpenAI, Anthropic, DeepSeek via Azure AI Foundry, and Google Gemini, with no proprietary detection model of its own, but with no other named detection engine and no published rule taxonomy, there's no way to evaluate what its security layer actually does. Panto offers neither an engine disclosure nor a third-party benchmark. Panto reports its findings by combining SAST, code-style linters, performance checkers, and secret scanners into one workflow, so the count folds linting and style into the same bucket as security.
Pricing runs $15 per developer per month on the standard plan, up to $40 on higher tiers, with some plans capped at 200 PRs per month, no free tier, and no bring-your-own-key. Reported traction is 500-plus developers and 5 million-plus lines of code, which is modest.
Best for: Teams that want business-context-aware PR review and engineering-manager analytics, with bundled security scanning as a secondary benefit, rather than teams evaluating SAST on detection depth.
