The Best AI‑Powered SAST in 2025

Static application security testing (SAST) tools promise to catch vulnerabilities before deployment, but traditional scanners rely on brittle pattern matching and struggle with business‑logic flaws. In 2025 vendors are augmenting or replacing rule‑based engines with artificial intelligence (AI).

This article compares seven leading SAST solutions—Corgea, Snyk Code, Semgrep, GitHub Advanced Security (CodeQL), Checkmarx, Fortify Aviator and Veracode—to understand how each uses AI, how they handle false positives and what features matter for developers and security teams.

What makes a SAST tool “AI‑powered”?

Many vendors market AI features, but not all AI is the same.

• AI‑native scanners use large language models (LLMs) or other machine‑learning engines to parse code semantically and reason about control/data flows across files. They can understand domain‑specific business logic and recognize patterns that rules miss.

• AI‑assisted scanners bolt generative AI on top of traditional engines. They still rely on rule/pattern matching for detection but use AI for triage or auto‑fix suggestions.

• Other factors that matter when choosing a SAST tool include:

• Accuracy and false positives – developers ignore noisy tools.

• Business‑logic and code‑logic coverage – can the tool detect issues beyond known dangerous sinks?

• Programming‑language support and integration with IDEs and CI pipelines.

• Custom policies – do you need to write custom rules in a DSL, or can you describe policies in natural language?

• Remediation and auto‑fix – does the tool generate context‑aware fixes?

Corgea – an AI‑native SAST scanner

Corgea is an AI‑native SAST tool. It relies on large language models to understand code contextually and scans 20+ languages (Java, JavaScript, Go, Python, C#, C++, Kotlin, PHP, etc.)[1]. Instead of pattern matching dangerous sinks, Corgea models data and control flows across modules, enabling it to detect business‑logic flaws such as broken authentication or insecure authorization[2].
Key features include:

• Low false‑positive rate (<5 %) – the LLM‑based engine triages around 30 % of findings, reducing noise.

• AI‑powered auto‑triage and auto‑fix – the scanner prioritizes critical issues and suggests code changes. An independent report by Latio.tech, found Corgea was the best auto-fixing tool on the market.

• SAST reachability analysis - the scanner can resolve endpoints and create a call graph to the vulnerable function to show if it is reachable or not.

• PolicyIQ – security teams can define custom policies in natural language instead of writing rule code. Policies translate to checks across the codebase[3].

• BLAST module – uses LLMs to analyze code semantics and find business‑logic weaknesses (e.g., broken access control) that pattern‑based tools miss[4].

• Integration – runs in CI pipelines and developer IDEs, providing context‑aware remediation inside pull requests.

Corgea’s AI‑native approach gives it advantages over rule‑based scanners in catching logical flaws. However, as a relatively new product, independent benchmarking data is limited; prospective users should evaluate it on their own codebases.

Snyk Code – developer‑first scanning with hybrid AI

Snyk Code is the SAST component of Snyk’s broader platform. It emphasizes developer experience, real‑time IDE feedback and broad language support (over 19 languages). Snyk’s DeepCode AI engine uses multiple machine‑learning models trained on 25 million+ data‑flow cases[5] to understand code semantics and generate fixes. Important points:

• Hybrid AI (symbolic + generative) – Snyk combines traditional static analysis with machine‑learning models to achieve high accuracy and avoid hallucinations[5].

• Low false‑positive rate – Snyk claims its learning models deliver precise results and that developers can provide feedback to improve detection. A survey on Sanj.dev notes that Snyk’s machine‑learning models provide low false‑positive rates and real‑time scanning in IDEs[6].

• Autofix – Snyk Code offers 80 %-accurate fix suggestions and continuous learning from millions of cases[7]. However, in the Latio.tech report, it was discovered that Snyk can only generate fixes during a rescan in the IDE extensions rendering it useless.

• Integration – Snyk integrates with IDEs, Git repositories and CI pipelines; it also covers open‑source dependencies, containers and IaC.

Independent reviews highlight Snyk’s ease of use and developer‑centric workflows but note that customizing rules may require Rego or Snyk’s own DSL[3]. Because Snyk is a commercial platform, cost may be a factor for small teams.

Semgrep – fast, open‑source with AI‑assisted remediation

Semgrep is a pattern‑matching SAST tool that gained popularity for its speed and flexible rule syntax.
It’s open source with commercial add‑ons and supports 40+ languages[8]. While originally deterministic, Semgrep introduced AI‑powered features in its Semgrep Assistant:

• Noise filtering & AI triage – Semgrep uses data‑flow reachability analysis and AI to hide likely false positives and learn from previous triage, reducing noise by up to 98 % (paid plans).[9].

• AI‑powered fixes – the assistant proposes code changes with contextual explanations and can create pull‑request fixes[10]. However, in the same Latio.tech report, the assistant was unable to reliably produce fixes.

• Custom rules – developers can write rules in YAML; the assistant helps classify findings and applies component tags[11].

• Speed and integration – Semgrep scans run quickly (median CI scan time around 10 seconds)[12] and integrate into IDEs, CI pipelines and GitHub/GitLab.

Independent research by Doyensec evaluated Semgrep vs. CodeQL using OWASP benchmarks. They found that Semgrep’s configuration produced zero false positives in security‑focused mode, whereas CodeQL achieved better detection but at a higher false‑positive rate[13]. Semgrep’s “auto” mode generated thousands of findings, underscoring the need to curate rules, but targeted configurations were more precise[14]. The researchers concluded that Semgrep suits security consultants who can manually vet findings and value ease of setup[15].

GitHub Advanced Security (CodeQL + Copilot Autofix)

GitHub’s native SAST is part of GitHub Advanced Security (GHAS).
It relies on CodeQL, a semantic code analysis engine that constructs a database of the code and runs queries to find vulnerabilities. GitHub offers thousands of curated CodeQL queries that are selected for high accuracy and a low false‑positive rate[16]. Key features include:

• Semantic analysis with CodeQL – CodeQL traces data flows across functions and modules[17] and supports languages such as Java, C/C++, C#, Go, JavaScript/TypeScript, Python, Ruby and Swift[18]. It is free for open‑source projects but requires a paid license for private repositories[19].

• Copilot Autofix – GitHub’s generative AI (powered by GPT‑4.1) automatically suggests code fixes for 90 % of alert types and claims three‑times faster remediation[17]. Suggested patches use the code‑scanning data and codebase context, and developers must review them; tests show that many suggestions can be committed with minimal edits[20].

• Secret scanning & push protection – GHAS monitors code for secrets and prevents them from being pushed to repositories[21].

Independent evaluations note that CodeQL can generate many false positives and slow scan times without heavy customization[22]. Doyensec’s comparison found that CodeQL detected more vulnerabilities than Semgrep but had a higher false‑positive rate, and its setup was more laborious; they recommend it for organizations willing to invest time and licensing costs[15].

Checkmarx – AI‑enabled scanning with Auto Remediation

Checkmarx One provides a comprehensive application security platform with SAST, SCA, IaC and other modules. The company has introduced several AI‑driven features:

• AI Security Champion and Auto Remediation – Checkmarx’ AI Security Champion plugin for SAST offers developers meaningful remediation guidance directly inside their IDE. The new auto‑remediation functionality provides detailed recommendations on how to resolve specific SAST vulnerabilities and integrates copy‑and‑paste code fixes[1]. This aims to shorten the time and effort required to fix vulnerabilities.

• AI Query Builder – developers and AppSec teams can write custom SAST queries using natural‑language prompts. The AI Query Builder generates and tunes SAST rules and claims to reduce false positives by up to 90 %[5].

• Reduced noise & faster scans – Checkmarx markets its AI‑enabled SAST as “up to 90 % faster with 80 % lower false positives”[2]. Its SAST product also offers incremental scanning and Best Fix Location (BFL) features to prioritize remediation.

• Language and integration support – according to vendor materials, Checkmarx supports more than 35 languages and 80 frameworks[6] and integrates with major IDEs, CI/CD systems and ticketing tools.

• Confidence scores and exploitability – the AI security champion provides an exploitability score (0–100) and explanations for each vulnerability[1].

Independent voices offer a more nuanced picture. Prospective users should therefore pilot Checkmarx on their codebase and calibrate its rules.

Fortify Aviator – AI‑powered auditing and contextual remediation

OpenText Fortify has added an AI‑powered component called Fortify Aviator (available to Fortify on Demand customers). Aviator combines LLM‑powered code analysis with Fortify’s two decades of SAST experience:

• Fast auditing and contextual fixes – Aviator embeds into the developer environment, performs audits and provides tailored code‑fix suggestions for SAST vulnerabilities[7]. These suggestions go beyond standard recommendations by including concrete code snippets[7].

• False‑positive reduction – Fortify Aviator’s prediction model learns from past fixes and developer feedback to improve accuracy over time and reduce false positives[8]. The AI classifies findings as true vulnerabilities or false positives and explains why[9]. An OpenText community blog notes that Fortify acknowledges that SAST false positives are inevitable and encourages users to leverage scan policies, filter sets and the AI‑powered Aviator to manage them[10].

• Deep scans & LLMs – the solution combines the latest generation of large language models with Fortify’s 20‑year SAST database, enabling deep semantic scans while providing faster remediation[11]. Fortify emphasises that developers remain in control of their code while receiving AI‑guided fixes[12].

• Deployment flexibility – the Aviator tool can be deployed in the cloud, on‑premises or as a managed service[13].

Fortify Aviator does not publicly publish a precise false‑positive rate; however, independent industry commentary notes that Fortify acknowledges false positives are unavoidable and offers rule tuning and prioritization features[10].

Veracode – AI‑assisted remediation and low noise

Veracode offers a mature SAST platform with AI‑assisted remediation. Key capabilities include:

• Industry‑leading low false‑positive rate – Veracode claims its static analysis engine produces a 1.1 % false‑positive rate, allowing developers to focus on real threats[4][3].

• Reachability analysis – the platform traces data flows to show whether tainted data can reach sensitive sinks, providing precise, actionable findings[14].

• AI‑assisted remediation – Veracode Fix uses machine‑learning models and a retrieval‑augmented generation (RAG) technique to generate secure patches. It analyzes the CWE ID, programming language, sink function and surrounding code context, then returns up to five code patches per flaw for developers to apply[15]. Developers can review and apply these fixes directly in the CLI or IDE[16]. Veracode notes that some flaws may remain due to false positives, and developers should mark them in the platform[17].

• Broad language and integration support – Veracode supports over 100 languages and frameworks[18] and provides integrations with more than 40 DevOps tools and IDEs[19]. It also offers real‑time scanning via IDE plugins and a unified platform covering SAST, SCA, DAST and container security[20].

Independent reports and customer surveys consistently highlight Veracode’s low noise and effective remediation. A 2025 VDC Research “Voice of the Engineer” survey recognised Veracode as a platinum vendor, citing its 1.1 % false‑positive rate, reachability analysis and AI‑assisted remediation[4].

Comparing SAST solutions

Choosing the right tool

The best AI‑powered SAST tool in 2025 depends on your organization’s priorities.

• Cutting‑edge AI and business‑logic detection: Corgea stands out. Its LLM‑based engine aims to understand application context and produce low‑noise results with high‑accuracy fixes. This makes it appealing for organizations struggling with false negatives and complex logic flaws. However, being a newer entrant, potential users should conduct pilots.

• Developer‑centric workflows: Snyk Code provides a polished experience with real‑time IDE feedback, broad language support and strong integration with container/IaC scanning. Its hybrid AI offers accurate auto‑fixes and low false positives. It may be ideal for teams looking for a unified platform covering open‑source and container security.

• Open‑source customization and speed: Semgrep remains a favorite among security engineers. It is lightweight, fast and customizable. The addition of Semgrep Assistant brings AI‑powered triage and fixes, although users still need to tune rules. It’s a good choice for teams that value transparency and control.

• GitHub‑centric development: For organizations already invested in GitHub, GitHub Advanced Security integrates seamlessly. CodeQL’s semantic analysis is powerful, and Copilot Autofix provides AI‑driven remediation. Yet, the tool supports fewer languages, can be slow on large repositories and may require significant rule tuning to reduce false positives.

• AI‑enabled scanning with flexible query generation: Checkmarx offers AI‑driven query builders and auto‑remediation that promise faster scans and fewer false positives. The ability to write queries in plain language is appealing. However, independent data suggest that false positives may still be high[3], so organizations should test it thoroughly.

• AI‑powered auditing and contextual remediation: Fortify Aviator leverages LLMs and decades of SAST expertise to classify findings, explain why they’re real or false positives, and propose complete code fixes[12]. It’s suited to teams that already use Fortify and want AI to reduce triage time and provide actionable patches. Users should be aware that false positives are still inevitable[10].

• Low noise with AI‑assisted remediation: Veracode offers one of the lowest false‑positive rates in the industry (<1.1 %), reachability analysis and AI‑assisted remediation via Veracode Fix[4]. It’s a strong choice for organizations prioritizing accuracy and comprehensive platform coverage, though it may be costlier for small teams.

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Final thoughts

AI is reshaping SAST. AI‑native tools like Corgea promise deeper insights and fewer false positives by using LLMs to understand code semantics and business logic. Hybrid AI tools like Snyk Code and AI‑assisted platforms like Semgrep, GitHub Advanced Security, Checkmarx, Fortify and Veracode bring automation and efficiency to established static analyzers. When selecting a SAST solution, evaluate not only marketing claims but also how the tool performs on your codebase, its language coverage, integration into your development workflow and the balance between detection breadth and false positives.

References

[1] [2] Corgea - AI-Native SAST Scanner

https://corgea.com/scanning

[3] Corgea vs Snyk: Which SAST Tool Fits Modern Development Best?

https://corgea.com/compare/corgea-vs-snyk

[4] How AI Code Scanning Breaks SAST’s Limits - Corgea as an Example | by Mohamed AboElKheir | AppSec Untangled | Medium

https://medium.com/appsec-untangled/how-ai-code-scanning-breaks-sasts-limits-corgea-as-an-example-6f8c9424f165

[5] DeepCode AI | AI Code Review | AI Security for SAST | Snyk AI | Snyk

https://snyk.io/platform/deepcode-ai/

[6] AI Code Security: Snyk vs Semgrep vs CodeQL | sanj.dev

https://sanj.dev/post/ai-code-security-tools-comparison

[7] Snyk Code | SAST Code Scanning Tool | Code Security Analysis & Fixes | Snyk

https://snyk.io/product/snyk-code/

[8] [9] [10] [22] [23] [24] Semgrep vs Github Advanced Security | Semgrep

https://semgrep.dev/resources/semgrep-vs-github/

[11] Overview | Semgrep

https://semgrep.dev/docs/semgrep-assistant/overview

[12] Semgrep App Security Platform | AI-assisted SAST, SCA and Secrets Detection

https://semgrep.dev/

[13] [14] [15] [19]  Comparing Semgrep and CodeQL · Doyensec's Blog 

https://blog.doyensec.com/2022/10/06/semgrep-codeql.html

[16] Understanding GitHub Advanced Security for application security testing - GitHub Resources

https://resources.github.com/learn/pathways/security/essentials/application-security-testing-github-advanced-security/

[17] GitHub Code Security · GitHub

https://github.com/security/advanced-security/code-security

[18] [20] Responsible use of Copilot Autofix for code scanning - GitHub Docs

https://docs.github.com/en/code-security/code-scanning/managing-code-scanning-alerts/responsible-use-autofix-code-scanning

[21] Introducing GitHub Secret Protection and GitHub Code Security - GitHub Changelog

https://github.blog/changelog/2025-03-04-introducing-github-secret-protection-and-github-code-security/

[25] Checkmarx blog describing new AI Security Champion features, including AI Query Builder and auto remediation; the article notes that the AI Query Builder reduces false positives and that the AI Security Champion provides remediation guidance and exploitability scores[1].

[26] Checkmarx knowledge‑hub article stating that Checkmarx SAST is up to 90 % faster with 80 % lower false positives[2].

[27] DEVOPSdigest news release noting that Checkmarx’s AI Query Builder generates custom SAST queries and claims to reduce false positives by up to 90 %[5].

[29] OpenText product page describing Fortify Aviator (Application Security Aviator). It explains that the tool embeds into the development environment, uses AI code analysis to continuously monitor code for security issues and provides contextualized code‑fix suggestions[7]; it also notes that the prediction model learns from past fixes to reduce false positives[8].

[30] DEVOPSdigest article announcing Fortify Aviator. The article states that Aviator accurately identifies true vulnerabilities vs. false positives and provides fully contextualized remediation (blocks of code) to fix issues[21]; it also notes that Aviator combines LLMs with 20 years of SAST experience to deliver deep, accurate scans and faster remediation[22].

[31] SiliconANGLE news report on Fortify Aviator. It highlights that the service identifies whether a vulnerability is a true positive or false positive and provides contextualized remediations, addressing one of the most time‑intensive tasks in application security[23].

[32] Veracode blog (“Engineers Speak”) summarizing why engineers rate Veracode highly. It lists features such as industry‑leading accuracy with a 1.1 % false‑positive rate, reachability analysis, broad language support and AI‑assisted remediation via Veracode Fix[4].

[33] Veracode documentation on Veracode Fix. It explains that Fix uses a machine‑learning model and retrieval‑augmented generation to generate secure patches; it analyzes CWE IDs, programming languages, sink functions and code context, and returns up to five patches per flaw[15]; developers can apply these patches directly and the system discards uploaded data[16].

[34] Codacy blog on Veracode alternatives. It notes that Veracode provides AI‑assisted flaw remediation, supports over 100 languages and frameworks, integrates with 40+ tools and guarantees a false‑positive rate of <1.1 %[20].

[1] Just Launched: Checkmarx AI Security

https://checkmarx.com/blog/just-launched-checkmarx-ai-security/

[2] Leveraging AI To Enhance Static Code Analysis (SAST)

https://checkmarx.com/learn/sast/how-ai-enables-more-effective-static-application-security-testing/

[4] [14] [18] [19] Veracode SAST and SCA Recognized by Engineers

https://www.veracode.com/blog/engineers-speak-veracode-sast-and-sca-plantinum-vendor/

[5] Checkmarx Announces AI Query Builders and AI Guided Remediation | DEVOPSdigest

https://www.devopsdigest.com/checkmarx-announces-ai-query-builders-and-ai-guided-remediation

[6] The 7 Best AI-Powered AppSec Tools You Can’t Ignore

https://www.instabug.com/blog/top-ai-development-tools-mobile-appsec

[7] [8] [13] AI Code Fixer and Code Analysis | OpenText Application Security Aviator

https://www.opentext.com/products/application-security-aviator

[9] [11] [12] [21] [22] OpenText Fortify Aviator Released | DEVOPSdigest

https://www.devopsdigest.com/opentext-fortify-aviator-released

[15] [16] [17] About Veracode Fix | Veracode Docs

https://docs.veracode.com/r/About_Veracode_Fix

[20] Top 5 Veracode Alternatives in 2024

https://blog.codacy.com/veracode-alternatives

[23] OpenText launches AI-powered Fortify Aviator to enhance code security and vulnerability remediation - SiliconANGLE

[24] https://siliconangle.com/2024/07/16/opentext-launches-ai-powered-fortify-aviator-enhance-code-security-vulnerability-remediation/