32 Best SAST Tools (2026)

What is SAST?

SAST (Static Application Security Testing) is a white-box security testing method that analyzes source code, bytecode, or binaries for vulnerabilities without executing the application.

It scans code before deployment, identifying flaws like SQL injection, cross-site scripting (XSS), and buffer overflows at the exact file and line number where they exist.

SAST tools parse code into abstract syntax trees (ASTs), then apply rule engines, data flow analysis, and semantic checks to detect these vulnerabilities automatically.

Developers plug SAST tools into their IDEs or CI/CD pipelines to catch these code-level issues before anything ships. Because the analysis happens on the source code itself, SAST does not need a running application, a test environment, or network access — which makes it fast to run and easy to automate.

Unlike DAST tools that test running applications from the outside, SAST works at the code level and does not need a deployed environment.

The trade-off is that SAST cannot detect runtime or configuration issues — a misconfigured web server, an exposed admin panel, or a broken authentication flow will slip past it.

That is why many teams run it alongside DAST or IAST for fuller coverage.

How Does SAST Work?

SAST works by parsing source code into an abstract syntax tree (AST), a structured representation that normalizes code regardless of programming language, and then applying multiple layers of analysis to find security flaws.

The process starts with a rule engine that matches known vulnerability patterns, then goes deeper with semantic analysis, data flow tracking, and control flow validation.

These seven techniques — from simple pattern matching to deep inter-procedural data flow analysis — determine a tool’s detection accuracy, scan speed, and price point.

Understanding how these techniques differ is what lets you tell a lightweight linter from a deep-analysis engine.

1

Abstract Syntax Tree (AST) Parsing

The tool parses your source code into an AST — a common format regardless of language — enabling faster and language-agnostic vulnerability detection.

2

Rule Engine

Applies language-specific, framework-relevant, and custom rules to identify security issues. Tools like Semgrep make it easy to write your own rules.

3

Semantic Analysis

SAST tools will look for the usage of insecure code and can even detect indirect calls that simple pattern matching would miss.

4

Structural Analysis

Checks for language-specific secure coding violations and detects improper access modifiers, dead code, insecure multithreading, and memory leaks.

5

Control Flow Analysis

Validates the order of operations by checking sequence patterns. It can identify dangerous sequences, resource leaks, race conditions, and improper initialization.

6

Data Flow Analysis

The most powerful technique. It tracks data flow from taint sources (attacker-controlled inputs) to vulnerable sinks (exploitable code), detecting injection flaws, buffer overflows, and format-string attacks. Enterprise tools like Coverity and Fortify perform deep inter-procedural data flow analysis across entire codebases.

7

Configuration Analysis

Checks the application's configuration files (XML, Web.config, .properties, YAML) and finds known security misconfigurations that code-only scanning would miss.

Which technique matters most depends on what you are trying to find.

Pattern matching and rule engines catch the obvious stuff fast: hardcoded passwords, deprecated crypto functions, missing input validation on clear entry points.

These run in seconds and work well as pre-commit hooks or quick CI scans. Semantic and structural analysis go deeper.

They understand how your code actually behaves, whether a variable holds user-controlled input, whether an access modifier exposes an internal method.

But they take more time and need a richer model of your language.

What Is Data Flow Analysis in SAST?

Data flow analysis is the most accurate SAST detection technique. It tracks data from taint sources (HTTP parameters, database reads, environment variables) through the program’s execution paths to vulnerable sinks (SQL queries, file writes, HTML output), catching injection vulnerabilities that span multiple files and function calls.

This is how enterprise tools find second-order SQL injection: malicious input enters in one request and gets executed in a completely different code path.

Most commercial SAST tools combine several of these techniques in a single scan. Checkmarx and Coverity run data flow, control flow, and semantic analysis together, cross-referencing findings to cut false positives. Snyk Code adds machine learning on top of semantic analysis to prioritize findings based on patterns from millions of real-world fixes. This layering is what separates a deep-analysis engine from a fast linter, and it is also what drives scan time and resource requirements.

Not every tool does all seven.

Free open-source tools like Bandit and Brakeman mostly stick to rule engines and pattern matching. For many teams, that is enough, especially when combined with Semgrep CE for custom rules.

If you need cross-file taint analysis, Semgrep Code extends the engine with deeper inter-file capabilities.

Enterprise tools like Checkmarx, Coverity, and Fortify layer all seven techniques together. That is a big part of why they cost what they cost.

Quick Comparison

I track 32 SAST tools across three license tiers: 13 free open-source, 7 freemium, and 8 commercial.

The SAST market in 2026 spans everything from free tools like Semgrep CE and Bandit that cover most CI/CD use cases, all the way to enterprise platforms like Checkmarx One and Veracode with compliance dashboards, ASPM correlation, and support for 35+ programming languages.

The table below groups them by license type so you can narrow down your shortlist quickly.

For full reviews, see each tool’s page on our mega comparison.

How Do You Integrate SAST into a CI/CD Pipeline?

SAST integrates into CI/CD pipelines by running automated code scans on every pull request, blocking merges when critical vulnerabilities appear, and posting findings as inline code annotations where developers can act on them immediately. I break this into four layers: pre-commit hooks for instant feedback, PR-level scanning for full analysis, quality gates for enforcement, and baseline management for handling legacy code.

The real payoff comes when every pull request gets scanned automatically before it merges. The goal: make security feedback as routine as unit tests.

Developers see findings before code gets approved, not weeks later in a security review.

Pre-commit hooks are the fastest feedback loop. Tools like Semgrep CE and Bandit run in seconds and catch obvious issues before code even leaves the developer’s machine.

Semgrep CE’s CLI scans an average-sized project in under 10 seconds, which makes it practical as a git pre-commit hook without slowing anyone down. This layer is not meant to be comprehensive.

It catches the easy stuff so the heavier scans downstream have less noise to deal with.

Pull request scanning is where most teams get the biggest value. Running a full SAST analysis on every PR through GitHub Actions, GitLab CI, or Jenkins means every code change gets a security review before merge.

Most tools post findings directly as PR comments or inline code annotations, so developers see the issue in context. GitHub CodeQL does this natively for GitHub repositories, uploading results as code scanning alerts on the pull request’s “Security” tab. Snyk Code and Semgrep CE both offer GitHub Actions that work the same way.

Quality gates add enforcement. Instead of just reporting findings, you block the merge when critical or high-severity vulnerabilities show up. SonarQube has built-in quality gate conditions that check for new security hotspots, and Checkmarx lets you define policies that prevent merging when specific CWE categories are detected.

Start strict only on critical findings and loosen gradually. Blocking on every medium-severity issue will make developers resent the tool.

Baseline management keeps the noise manageable. When you first introduce SAST to an existing codebase, the initial scan will produce hundreds or thousands of findings.

Do not dump all of them on the team. Baseline the existing findings and configure the pipeline to only flag new issues introduced by the current PR. SonarQube calls this the “new code period.” Bandit supports baseline files that exclude known findings.

Over time, you chip away at the backlog through separate remediation sprints.

How Long Does a SAST Scan Take?

SAST scan times range from seconds for lightweight tools to several hours for deep-analysis engines, depending on the tool and codebase size.

Lightweight scanners like Semgrep CE and Bandit finish in seconds to minutes even on large codebases.

Full deep-analysis scans with tools like Checkmarx or Fortify can take 15 minutes to several hours depending on codebase complexity.

A scan that takes 45 minutes on every PR will get disabled within a week. I have seen it happen.

Most tools support incremental scanning, analyzing only the files that changed rather than the entire codebase, which cuts scan times by 80-90%.

According to Veracode’s documentation, Veracode Pipeline Scan returns results with a median scan time of 90 seconds by focusing on the diff. Semgrep CE can be configured to scan only changed files using --baseline-commit. Mend SAST offers three scan profiles (Fast, Balanced, Deep) that trade thoroughness for speed.

For monorepos, the challenge is avoiding full-codebase scans when only one service changed. Most CI systems support path-based triggers.

You can configure GitHub Actions to run a SAST job only when files in a specific directory change.

Pair this with incremental scanning and a large monorepo gets SAST feedback in minutes instead of hours. SonarQube and Checkmarx also support project-level configuration that maps subdirectories to separate scan targets.

A typical GitHub Actions setup runs Semgrep CE on every pull request, uploads SARIF results to GitHub’s code scanning dashboard, and blocks the merge if new critical findings appear. The whole workflow adds about 30–60 seconds to the CI pipeline for most repositories — negligible compared to build and test times.

What Is AI-Powered SAST?

AI-powered SAST refers to static analysis tools that use machine learning, large language models, or AI agents to improve vulnerability detection, reduce false positives, or generate automated fix suggestions. In 2026, AI capabilities in SAST tools fall into three distinct categories: AI-assisted triage and remediation, semantic query engines, and agentic SAST that scans code inside AI editors before it reaches the repository.

AI-assisted SAST tools still use traditional rule-based engines or semantic analysis for detection, but they layer AI on top for triage, prioritization, and auto-fix suggestions. Snyk Code uses its DeepCode AI engine, trained on millions of real-world commits, to suggest one-click fixes alongside each finding. Checkmarx One offers Checkmarx One Assist, a family of agentic AI agents including Developer Assist (real-time IDE security), Policy Assist (automated policy management), and Insights Assist (risk intelligence). SonarQube added AI CodeFix that generates LLM-powered remediation suggestions.

The detection engine in these tools is still deterministic rules and data flow analysis. AI handles the “what do I do about it?” part.

Semantic query engines take a different approach entirely. GitHub CodeQL treats your entire codebase as a relational database, compiling source code into a queryable representation of variables, functions, types, and data flows.

Instead of matching patterns, you write declarative queries that describe the vulnerability you are looking for.

CodeQL can find complex multi-step vulnerabilities (a tainted value passing through 5 functions across 3 files before reaching a SQL query) that pattern-matching tools miss entirely.

The trade-off: writing custom CodeQL queries requires learning a dedicated query language, which is steeper than Semgrep CE’s code-mirroring syntax.

Agentic SAST is the 2026 frontier. Tools like Mend SAST plug directly into AI code editors via MCP (Model Context Protocol) servers, integrating with Cursor, Claude Code, GitHub Copilot, Windsurf, and Amazon Q to scan AI-generated code before it even reaches your repo.

The logic is straightforward: if AI is writing your code, AI should also be checking it.

Checkmarx entered this space too, with its Developer Assist agent that runs inside Cursor, Windsurf, and alongside GitHub Copilot in VS Code.

This matters because AI-generated code introduces vulnerabilities at a comparable or higher rate than human-written code.

A 2021 NYU study found that roughly 40% of GitHub Copilot suggestions contained security vulnerabilities when generating security-sensitive code (Pearce et al., “Asleep at the Keyboard,” NYU 2021).

A follow-up Stanford study confirmed the pattern: developers using AI coding assistants produced less secure code than those writing it manually (Perry et al., Stanford 2023).

With AI coding assistants becoming standard development tools in 2025-2026, scanning their output with SAST is no longer optional.

Newer entrants are pushing AI further into the detection engine itself. DeepSource uses its Autofix AI to generate one-click remediation for detected issues, and according to DeepSource, its Narada model achieves 97% precision for secrets detection. Qodana (by JetBrains) brings 3,000+ IDE inspections to CI/CD pipelines with taint analysis that, per JetBrains’ benchmarks, processes 7 million lines in under 30 minutes. Both combine traditional static analysis with ML-based prioritization to surface findings most likely to be real vulnerabilities.

When evaluating tools in 2026, I ask three questions. Does the tool use AI in its detection engine, or only in its remediation UI?

Does it scan AI-generated code before it hits your repo? And does its AI produce fix suggestions that developers can apply in one click, or just generic descriptions of the problem?

How Do You Choose the Right SAST Tool?

Choosing the right SAST tool comes down to five factors: language and framework support, CI/CD integration, false positive rate, budget, and developer experience. The right tool for your team depends on your language stack, pipeline setup, and whether you need free open-source coverage or enterprise features like compliance dashboards and centralized policy management.

Here is what I would look at:

1. Language and framework support. This is the single most important filter.

A tool that does not understand your framework will miss vulnerabilities specific to its patterns, or drown you in false positives from patterns it misunderstands. Brakeman is the best example: it understands Rails routing, ActiveRecord queries, and ERB templates deeply, but it is Rails-only. Bandit covers Python with 47 built-in checks.

If you use multiple languages, look for multi-language tools. Semgrep CE covers 30+ languages, Checkmarx One covers 35+, and Veracode supports 36+ languages and 100+ frameworks including legacy stacks like COBOL and RPG.

2. CI/CD integration. How easily does it plug into your pipeline?

Look for native support for GitHub Actions, GitLab CI, Jenkins, or Azure DevOps. GitHub CodeQL is the easiest to set up if you are already on GitHub.

It runs as a built-in Actions workflow with zero external configuration. Snyk Code and Semgrep CE both offer well-documented GitHub Actions that upload SARIF results to the code scanning dashboard.

Enterprise tools like Checkmarx and Fortify have plugins for every major CI system, but expect more configuration work upfront.

3. False positive rate. False positives are what kills SAST adoption in practice.

Developers stop looking at findings when half of them are noise. Commercial tools tend to be quieter out of the box because they invest in data flow analysis and ML-based prioritization.

According to Cycode’s published benchmarks, Cycode achieves a 2.1% false positive rate on the OWASP SAST Benchmark.

Open-source tools like Semgrep CE can reach similar precision, but you need to invest time writing custom rules tuned to your codebase.

4. Budget.

Free open-source SAST tools cover most use cases for small and mid-size teams. Semgrep CE handles multi-language scanning with custom rules. Bandit and Brakeman cover Python and Rails specifically. SonarQube CE provides code quality plus security across 19 languages. CodeQL is free for public repos.

Enterprise tools add centralized reporting, compliance dashboards (PCI DSS, SOC 2, HIPAA mapping), cross-project portfolio views, and dedicated support. But honestly, the free options have gotten good enough that many teams never upgrade.

5. Developer experience.

IDE integration, clear fix guidance, and fast scan times keep developers from ignoring findings. Snyk Code does well here with real-time scanning in VS Code, IntelliJ, and PyCharm plus AI-powered fix suggestions from its DeepCode engine. Qodana brings the same JetBrains IDE inspections developers already see locally into the CI/CD pipeline.

In my experience, tools that show findings as inline code annotations in pull requests get far higher fix rates than tools that send email reports to a separate dashboard.

Which SAST Tool Should You Pick?

If you are a startup or small team — Start with Semgrep CE plus Bandit. Free, fast, and you can set them up in GitHub Actions in under 10 minutes.

Add SonarQube CE later if you want code quality metrics alongside security findings. See my open-source SAST tools guide for the full comparison with language tables, CI/CD setup, and detection quality benchmarks.

If you are an enterprise with legacy code — Fortify (44+ languages including COBOL, ABAP, Fortran) or Checkmarx One (35+ languages with ASPM correlation) handle the broadest language stacks. Veracode is worth a look if you need binary analysis. It scans compiled bytecode across 36+ languages and 100+ frameworks without requiring source code access, which is useful for third-party code audits.

If you are already on GitHub — CodeQL is free for public repositories and integrates natively with GitHub Actions and code scanning alerts. Private repos need a GitHub Advanced Security license. It covers 12 languages with deep semantic analysis.

If developer experience is the priority — Snyk Code offers real-time IDE feedback with AI-powered fix suggestions. The free tier works for individual developers, and the paid platform bundles SAST with SCA, container, and IaC scanning.

If you need compliance reporting — Coverity (Black Duck) maps findings to MISRA, AUTOSAR, ISO 26262, CERT, and DISA STIG standards. Fortify and Checkmarx both offer PCI DSS 4.0 and OWASP Top 10 2021 compliance reports out of the box. Worth noting: PCI DSS 4.0 Requirements 6.2.4 and 6.3.2 mandate addressing common coding vulnerabilities and reviewing custom code before release, so SAST with compliance mapping is a direct regulatory need.

What Are the Best Practices for SAST?

The most common SAST failure mode is not a bad tool – it is a good tool that nobody pays attention to because it was introduced poorly. These eight practices focus on reducing false positives, integrating scans into developer workflows, and measuring remediation outcomes rather than just finding counts.

1. Start with a baseline scan, then go incremental. Run a full scan once to get a snapshot of existing technical debt.

Triage the results: suppress known false positives, categorize genuine findings by severity, and create a backlog for the real issues.

Then switch to incremental scanning on every PR so developers only see findings they introduced. Nobody fixes 2,000 existing findings on day one.

Asking them to will guarantee they resent the tool. SonarQube handles this through its “new code period” setting, and Bandit supports baseline files that exclude previously seen findings.

2. Own your rules.

Default rule sets catch common vulnerability patterns, but your codebase has internal frameworks, custom authentication wrappers, and proprietary APIs that generic rules do not understand.

Write custom rules for these. Semgrep CE makes this straightforward. Its rule syntax mirrors your source code, so a developer can write a rule in minutes without learning a query language. CodeQL offers more expressive power through its declarative QL language for complex multi-step vulnerability patterns.

Teams that invest in 10-20 custom rules tailored to their stack see measurably better signal-to-noise ratios.

3. Set severity thresholds that match your risk appetite. Block merges on critical and high findings. Warn on medium. Ignore informational noise entirely.

Document these thresholds, get engineering and security to agree on them, and adjust over time as the team gets comfortable. Starting too strict creates pushback. Starting too lenient means findings pile up without action.

4. Make findings visible where developers work. PR comments beat email reports.

IDE warnings beat PR comments. The closer a finding is to the developer’s cursor, the faster it gets fixed. Snyk Code provides real-time IDE feedback in VS Code and IntelliJ. GitHub CodeQL posts findings as inline code annotations on pull requests.

The tools that win adoption are the ones that fit into the developer’s existing workflow, not the ones that require checking a separate dashboard.

5. Combine with DAST and SCA. SAST finds code-level flaws. DAST catches runtime and configuration issues. SCA covers your third-party dependencies.

Together, they give you real coverage instead of partial visibility. A SQL injection found by SAST becomes much more urgent when your SCA scan confirms the vulnerable ORM version is also affected by a known CVE.

See my SAST vs SCA guide for a detailed breakdown of how these two approaches complement each other.

6. Track fix rates, not just finding counts.

A tool that finds 500 issues nobody fixes is worse than one that finds 50 issues that all get resolved.

The metrics that matter: mean time to remediate (how fast do findings get fixed after detection?), fix rate (what percentage of findings actually get resolved?), and finding density per KLOC (are you improving over time?). Report these to engineering leadership monthly to keep security visible.

7. Build a security champion program. Assign one developer per team as a security champion.

Someone who takes ownership of SAST findings, helps triage false positives, and promotes secure coding practices within their team. Champions do not need to be security experts.

They just need to care enough to keep the team’s finding queue clean. This spreads security responsibility and prevents a single AppSec team from becoming a bottleneck.

8. Measure what matters: finding density and remediation time. Track findings per thousand lines of code (KLOC) across your repositories over time.

A decreasing trend means developers are writing more secure code, not just suppressing findings. Pair this with mean time to remediate.

If your MTTR is under 7 days for critical findings, your SAST program is working. If it is over 30 days, the tool is producing reports that nobody reads.

What Are the Most Common SAST Mistakes?

The most common SAST mistakes that kill adoption are running only default rules, ignoring framework-specific patterns, treating all findings equally, and scanning only on the main branch instead of on every pull request. Here is what each looks like and how to avoid it.

1. Running only default rules. Every SAST tool ships with a generic rule set designed to work across many codebases.

These rules catch common CWE patterns, but they miss vulnerabilities specific to your internal frameworks, custom authentication wrappers, and proprietary APIs.

If you use a custom ORM, a homegrown session management library, or framework middleware that generic rules do not model, those code paths go unscanned.

Invest time in writing custom rules — even 10–15 targeted rules for your most critical code paths will significantly improve detection coverage.

2. Ignoring custom framework patterns.

A SAST tool that does not understand your framework will produce both false positives (flagging safe framework-handled patterns) and false negatives (missing vulnerabilities in framework-specific code).

If your team uses Spring Security, Django REST Framework, or a custom authorization decorator, make sure your SAST tool has rules that model those patterns. Semgrep CE and CodeQL both let you define framework-aware rules.

Some commercial tools like Checkmarx let you add custom sanitizer definitions so their data flow engine correctly models your internal security functions.

3. Treating all findings equally.

A hardcoded test API key in a unit test file is not the same severity as a SQL injection in a production API endpoint.

Teams that treat every finding as equally urgent burn out quickly and start ignoring the tool. Prioritize based on exploitability, exposure (is the code reachable from the internet?), and data sensitivity.

Tools with ASPM capabilities like Checkmarx One and Cycode correlate findings with application context to help with this ranking automatically.

4. Not suppressing known false positives.

When the same false positive shows up on every scan, developers learn to ignore all findings — including the real ones.

Build a process for reviewing and suppressing confirmed false positives using inline comments (// nosec, // nolint, # nosemgrep) or centralized suppression rules.

Document why each suppression was added so it can be reviewed later.

A clean findings list with 20 real issues gets more developer attention than a noisy list with 200 items where half are noise.

5. Scanning only on the main branch. Running SAST only after code merges to main defeats the purpose of shift-left security.

By the time a finding surfaces, the code is already in production or queued for release. Run scans on every pull request so developers can fix issues before the code merges.

The incremental scan cost is minimal compared to the cost of finding a vulnerability in production.

6. Not correlating SAST findings with SCA and DAST results.

A SQL injection found by SAST in a function that uses a vulnerable database driver flagged by SCA is a much higher risk than either finding alone.

A reflected XSS found by SAST in a controller that DAST confirms is reachable from the internet is a confirmed vulnerability, not just a theoretical one.

Teams that analyze SAST, SCA, and DAST findings in isolation miss these compounding risk factors.

Unified platforms and ASPM tools help, but even without them, periodic cross-referencing of findings from different scan types improves prioritization.

Bandit

Open-Source Python Scanner

Free (Open-Source) 1 langs

Bearer

NEW

Data-First SAST with Privacy Scanning

Open Source (ELv2) / Part of Cycode

Brakeman

Open-Source Ruby on Rails

Free (Non-Commercial) 1 langs

Checkmarx

Gartner Leader for Enterprise SAST

Commercial

Codacy

40+ Languages with AI Code Protection

Commercial (Free for open-source, CLI is AGPL-3.0) 18 langs

Contrast Scan

SAST with Runtime Context

Commercial 20 langs

Coverity

Deep Analysis for Complex Codebases

Commercial 21 langs

DeepSource

AI-Powered Code Analysis with Autofix

Commercial (Free tier available) 16 langs

detect-secrets

Baseline secret management

Free (Open-Source, Apache-2.0)

Fortify Static Code Analyzer

Gartner Leader 11 Years, 33+ Languages

Commercial

GitHub CodeQL

Semantic Analysis, GitHub Native

Free for open-source, Commercial for private repos 12 langs

GitLab SAST

Built-in CI scanning

Included with GitLab (Free tier: limited, Premium/Ultimate: full features)

Gitleaks

Git secret scanner

Free (Open-Source, MIT)

gosec

Go Security Linter

Free/OSS 1 langs

Graudit

Grep-Based Code Auditing

Free (Open-Source, GPL-3.0) 10 langs

HCL AppScan

Gartner Leader with Free CodeSweep

Commercial (AppScan CodeSweep is Free) 18 langs

Horusec

Multi-Language Open-Source Orchestrator

Free/OSS (Apache 2.0) 14 langs

Kiuwan Code Security

30+ Languages Including Legacy

Commercial 31 langs

Klocwork

Safety-Certified C/C++ Analysis

Commercial (with Free Trial) 7 langs

Mend SAST

Agentic SAST for AI-Generated Code

Commercial 12 langs

NodeJSScan

Node.js Security Scanner

Free/OSS 2 langs

OpenGrep

NEW

Community Fork, Taint Analysis, 30+ Languages

LGPL-2.1 36 langs

PMD

Multi-Language Code Analyzer

Free/OSS 13 langs

PT Application Inspector

SAST+DAST+IAST+SCA Combined

Commercial 16 langs

Qodana

NEW

JetBrains IDE Inspections in CI/CD

Commercial (Free tier available) 14 langs

Semgrep

Free CE Engine + Commercial AppSec Platform

LGPL-2.1 (CE) / Commercial (Platform) 35 langs

Snyk Code

Developer-First SAST with AI-Powered Fix Suggestions

Commercial (Free tier available) 14 langs

SonarLint

Real-time IDE analysis

Free (LGPL-3.0) + Commercial Features with SonarQube/SonarCloud

SonarQube

35+ Languages, Code Quality + Security

Commercial (with Free Community Build) 23 langs

SpotBugs

Java Bug Pattern Detection

Free/OSS (LGPL-2.1) 4 langs

TruffleHog

Verify live secrets

Free (Open-Source, AGPL-3.0) + Commercial Plans

Veracode Static Analysis

Binary Analysis, No Source Needed

Commercial 16 langs

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SAST covers one aspect of application security. Browse other categories in our complete tools directory.

Suphi Cankurt

AppSec Enthusiast

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10+ years in application security. Reviews and compares 170 AppSec tools across 11 categories to help teams pick the right solution. More about me →