Detection is table stakes for SAST in 2026. We compared 10 tools on triage and review.

SAST tools are usually judged on detection: how many languages a scanner covers, how deeply it traces data flow. Detection is only the first third of the job of a modern SAST; a tool that finds 500 potential issues and dumps them into a queue is not helpful in 2026. A tool that finds 500, filters out 450 false positives with explainable reasoning, and surfaces the 10 that touch your authentication flows is a different product. Modern SAST encompasses at least three capabilities: detection, triage, and investigation. Where a vendor invests across those three decides whether their output is something you act on or another noisy queue you learn to ignore.

We evaluated 10 SAST tools across these layers to help you cut through vendor noise.

Top SAST products in 2026

Detection: who finds what

Every tool in this list runs static analysis. The differences are in how they do it, what languages they cover, and how deeply they trace data flow.

Checkmarx covers 35+ languages for SAST specifically, with incremental scanning on changed code. The engine is deterministic. AI is applied downstream, not at the detection layer. The breadth is real: if you're running COBOL alongside TypeScript, Checkmarx handles both.

Snyk Code takes a different approach. The DeepCode AI engine combines symbolic analysis with generative AI, trained on 25 million data-flow cases. Scans run in real-time inside the IDE as you type, no build step required. Language support is narrower at 11 languages, but the depth of analysis within those languages is strong. Worth noting: the initial scan uploads your source code to Snyk's cloud infrastructure, which is a dealbreaker for some organizations.

Veracode does binary analysis. You upload compiled artifacts, not source code. This means it works on languages other tools can't touch (COBOL, Visual Basic 6, Apex) and doesn't require source access. The tradeoff is speed. Zip-archive uploads don't work with pre-commit hooks, so Veracode fits better in a scheduled-scan or PR-gate workflow than a real-time one.

Semgrep runs pattern matching with interprocedural taint analysis in the Pro Engine. The rule language is the differentiator. You can write custom rules that match your codebase's patterns, and the community has produced 20,000+ Pro rules. Diff-aware scanning by default keeps it fast. OpenGrep, the open-source fork, maintains the LGPL 2.1 license for the core engine.

GitHub Advanced Security (CodeQL) treats code as a queryable database. You write QL queries that describe vulnerability patterns, and CodeQL finds every instance. Variant analysis is the standout: find one SQL injection, write a query, and catch every structural variant across the codebase. Limited to 9+ languages and locked to GitHub as a platform.

Black Duck now runs two engines. Coverity is the established deterministic scanner covering 22 languages. Signal, launched December 2025, adds LLM-based detection that's language-agnostic. Signal uses agentic AI workflows grounded in security knowledge bases to determine reachability and exploitability. It's young, and the two engines aren't fully unified yet.

SonarQube combines code quality and security analysis with 6,500+ rules. The detection engine is deterministic. Strong on quality gates in CI/CD. The Enterprise tier adds taint tracking and support for legacy languages (COBOL, ABAP, Apex). Pricing is LOC-based, which can climb unpredictably as codebases grow.

Endor Labs launched AI-native multi-modal SAST in November 2025. Detection agents use LLMs with full-repository context to find business logic flaws and context-dependent authorization bugs that rule-based scanners miss. The multi-agent architecture runs detection, triage, and remediation as specialized agents. Validated at Atlassian, Cursor, Dropbox, and Snowflake. The most aggressive AI-native detection approach among established vendors.

Aikido Security consolidates 15+ scanners into one platform, running a dual-engine approach with their proprietary Aikido Engine alongside OpenGrep. Cross-file analysis for 9 languages, within-file for 8 more. Pricing is a platform fee plus per-developer, keeping it more predictable than LOC-based models.

Fencer runs both classical deterministic scanners and AI-driven detection agents. The classical layer covers 30+ languages and handles exhaustive pattern matching: every instance of a dangerous pattern, found in seconds. The AI detection agents go further, using code context and application structure to identify issues that rule-based scanners can't express. Both layers feed into the triage and investigation pipeline downstream.

SAST Triage capabilities: who handles false positives, and how

This is where most tools diverge, and where the comparison gets interesting. Detection is a partially solved problem. Triage is where the industry is still figuring it out.

The false positive problem is universal. On real codebases, 90%+ of SAST findings are noise. Every vendor acknowledges this. What they do about it varies from nothing (dump findings into a queue) to black-box AI (suppress findings without explanation) to transparent, benchmarked triage (publish what the AI does and how often it's wrong).

Checkmarx applies AI downstream of detection through three agentic assistants (Developer Assist, Policy Assist, Insights Assist). The approach is AI-assisted rather than AI-native: the deterministic engine generates findings, and AI helps developers understand and act on them. Triage still requires significant manual tuning for organizations with complex codebases.

Snyk Code claims near-zero false positives from the detection layer itself, arguing that data-flow-sensitive analysis with inter-procedural reasoning prevents false positives from being generated in the first place. If true, this reduces the triage burden. The claim is hard to verify independently since Snyk doesn't publish benchmark data against labeled datasets.

Veracode Fix uses ML and retrieval-augmented generation against a proprietary remediation database. It returns up to 5 code patches per flaw and claims 60-70% developer acceptance rate. The focus is more on remediation than triage: Veracode helps you fix findings rather than helping you decide which ones are real.

Semgrep Assistant adds AI-powered triage that analyzes each finding and assesses exploitability. Semgrep publishes numbers: 20-40% false positive reduction out of the box, with research showing 94-98% FP elimination when combining LLM triage with the static analysis backbone. The "Memories" feature learns from past triage decisions. Among the established vendors, Semgrep is the most transparent about what its AI triage layer does.

GitHub Advanced Security pairs CodeQL with Copilot Autofix, which generates suggested fixes inline in PRs. Median fix time drops from 1.5 hours to 28 minutes. The approach skips triage and goes straight to remediation, which works when CodeQL's precision is high enough that most findings are real. CodeQL's query-based approach does tend toward lower false positive rates than pattern-matching scanners.

Black Duck Signal performs contextual analysis beyond pattern recognition. ContextAI uses multi-model LLMs grounded in security knowledge bases to determine reachability and exploitability. The approach is promising but young. Signal launched December 2025 and independent benchmark data isn't available yet.

SonarQube added AI CodeFix (auto-generated fixes via GPT-4o and Claude 3.7) and AI Code Assurance (detects AI-generated code and applies specialized analysis). The core detection engine remains deterministic. Triage is largely manual, aided by quality gates and severity categorization rather than per-finding AI reasoning.

Endor Labs runs triage agents that perform multi-modal analysis, comparing dataflow results with pattern-matching results to classify findings. They publish strong numbers: 95% false positive elimination with 4.5% verified as true positives and 0.5% ambiguous. The multi-agent architecture means detection and triage are tightly integrated rather than bolted together.

Aikido Security offers AI AutoTriage that filters irrelevant findings using context. One-click AI AutoFix covers 100+ issue types. The triage capabilities are present but not the primary differentiator. Aikido's strength is consolidation, not triage depth.

Fencer runs a dedicated LLM triage layer that processes every scanner finding with its surrounding code context: callers, callees, taint sources, and configuration. Each finding is classified three times with majority vote. The model is instructed to be conservative: uncertain findings get escalated to humans, never silently dismissed.

What makes Fencer's approach distinct is transparency. We published a benchmark testing 15 models (Claude, GPT, Kimi K3, DeepSeek, Mistral, and others) against 142 engineer-reviewed findings. The results are public: which models dismissed real vulnerabilities, which refused to analyze findings, and which struck the right balance. We advocate for open-weights models at this layer because content filtering on closed APIs blocks analysis of the very findings the scanner flagged as dangerous. No other vendor in this comparison publishes equivalent model-level benchmark data on their triage pipeline.

Fencer's triage guardrails are also configurable per-repository, per-severity, and per-finding-category. A finding in an authentication flow requires higher confidence to auto-clear than one in a logging utility. These rules are configuration your security team owns, not black-box model behavior.

Investigation: which static analysis tools go beyond pattern matching

Most SAST tools stop after detection and triage, however a smaller number attempt deeper analysis: looking for logic bugs, broken authorization, IDOR vulnerabilities, and other flaws that don't match a syntactic pattern.

Endor Labs is the furthest along here among the established vendors. Their detection agents use full-repository context to find business logic flaws and multi-step authorization issues. The multi-agent architecture is designed for exactly this kind of deeper analysis.

Black Duck Signal applies agentic AI workflows to determine reachability and exploitability, which overlaps with investigative analysis. The approach uses security knowledge bases to ground the LLM's reasoning, but it is still early.

Checkmarx, Snyk, Veracode, Semgrep, GitHub, SonarQube, and Aikido don't operate at this layer. They detect patterns, help you triage or fix them, and stop there. Logic bugs, broken authorization flows, and business rule violations are outside their scope.

Fencer runs investigative agents as a third layer, downstream of triage. These agents get broader context: API route definitions, middleware chains, database schemas, authentication flows. They answer open-ended questions: does this endpoint enforce authorization correctly? Can this state transition happen out of order? The agents are guided by configurable threat models that define the assets, trust boundaries, and attack scenarios specific to your application. This turns open-ended investigation into directed analysis.

The investigative layer is expensive (10-50x the cost of triage per investigation) and more speculative. It doesn't replace penetration testing. But it catches the class of bug that sits between what a scanner can pattern-match and what a pentester would find with unlimited time.

The missing layer: review workflows and compliance evidence

There is a gap in this market. Every vendor invests in finding vulnerabilities. Almost none invest in what happens after a finding lands in someone's queue.

In practice, the vulnerability review process at most organizations looks like this: a SAST tool produces findings. Someone exports them to Jira or Linear. An engineer looks at each one, decides whether it's real, and either fixes it or marks it as a false positive. If the organization has compliance requirements (SOC 2, ISO 27001, HITRUST), that triage decision needs to produce evidence: who reviewed it, when, what was the reasoning, what was the disposition. That evidence gets assembled manually, usually in a spreadsheet or a Confluence page, weeks later when an auditor asks for it.

None of the tools in this comparison have built the actual review workflow. They find vulnerabilities, some triage findings with AI, and a few generate fix suggestions, but the guided process of walking a team through their findings, recording disposition decisions with reasoning, and producing compliance-ready evidence as a byproduct of the review itself does not exist in any of these products.

This matters because the review step is where findings either get resolved or go to die. Without a structured workflow, findings pile up. Engineers batch-dismiss them. The ones that mattered get lost in the noise. And when the auditor shows up, the security team scrambles to reconstruct evidence for decisions that were made months ago in someone's head.

Fencer built the review workflow as a first-class capability. Findings flow through a guided review process where engineers and security teams triage, dismiss with recorded reasoning, escalate, or accept risk, all within the platform. Every decision produces compliance evidence automatically. When your SOC 2 auditor asks for evidence that your team reviews and dispositions SAST findings, you export the review log. You don't reconstruct it from ticket history and Slack threads.

This is the kind of capability that doesn't show up in a Gartner quadrant. It is a workflow feature, and it is the reason findings get resolved instead of aging in a backlog.

The full comparison

Where each tool leads

Enterprise breadth: Checkmarx. Broadest language coverage, deepest compliance story, 7-year Gartner Leader streak. If you're a Fortune 500 running 35 languages across regulated industries, Checkmarx is the safe pick.

Developer experience: Snyk. Real-time IDE scanning, no build required, context-rich explanations. The free tier is generous. If your priority is developer adoption over security team control, Snyk gets out of the way.

Legacy language support: Veracode. Binary analysis means you can scan COBOL and VB6 artifacts that other tools can't parse. FedRAMP authorized. If your codebase includes languages from the 1990s, Veracode handles them.

Custom rule authoring: Semgrep. The rule language is the best in class. If your security team wants to encode your own organization's patterns and anti-patterns, Semgrep gives you that control. The OpenGrep fork keeps the core engine open.

Platform-native scanning: GitHub Advanced Security. If your entire workflow lives in GitHub, CodeQL and Copilot Autofix integrate with zero friction. Variant analysis is powerful: one query catches every structural variant of a bug across the codebase. The lock-in to GitHub is the tradeoff.

AI-native detection: Endor Labs. The most aggressive use of LLMs at the detection layer. If you want AI finding bugs that rule-based scanners can't express, Endor Labs is pushing that boundary harder than anyone else in this list.

AI triage transparency and multi-layered SAST: Fencer. Classical scanners plus AI detection agents, LLM triage with published benchmarks and configurable guardrails, and investigative agents guided by threat models. Published model-level comparison data on the triage layer. Advocates for open-weights models that won't content-filter your security findings. The only tool in this comparison that combines all three layers with transparent, reproducible evaluation of each.

Vulnerability review workflow: Fencer. The only tool in this list with a guided review process that produces compliance evidence as a byproduct of triage. Every other vendor stops at finding or fixing. None of them help your team walk through findings, record dispositions with reasoning, and hand an auditor a clean evidence log.

SMB and mid-market value: Fencer. Built for teams that need SAST, SCA, secrets scanning, DAST, cloud posture, and compliance evidence in one platform without hiring a dedicated security team to operate it. Strong for SMBs (50-500 people) where the alternative is stitching together five separate tools, and a serious contender for mid-market (500-5,000 people) where the compliance evidence workflow replaces manual audit prep that burns weeks of security team time every quarter. Aikido plays in a similar space with its consolidated scanner approach, though without the multi-layered SAST pipeline or the review workflow.

What we learned

• "AI-powered" means different things. Some vendors use AI to explain findings. Some use it to generate fix suggestions. Some use it to suppress false positives. A few use it to find bugs that scanners can't express. Ask which one before you evaluate.
• Triage transparency is rare. Most vendors treat their AI triage as a black box. If a vendor can't tell you which model they use, how often it's wrong, and what it does when it's uncertain, you're trusting a pipeline you can't audit.
• Detection is table stakes. Triage is the differentiator. Every tool in this list finds potential vulnerabilities. What separates them is what happens next. The gap between "here are 500 findings" and "here are the 10 that matter, and here's why" is where value gets created or destroyed.
• Investigation is the next frontier. A few vendors are pushing into logic bugs and authorization flaws that scanners can't pattern-match. This layer is expensive and early, but it catches the bugs that get exploited. Expect more vendors to move here over the next year.
• Nobody built the review workflow. Every vendor invests in finding vulnerabilities. None of them built the process for resolving them with compliance evidence attached. The gap between "finding generated" and "auditor satisfied" is filled with spreadsheets and Slack threads at most organizations. That's a product problem, not a process problem.
• Enterprise and mid-market need different things. A Fortune 500 needs 35+ language support, compliance frameworks, and a Gartner quadrant to point at during procurement. A mid-market company (500-5,000 people) needs one tool that covers scanning, triage, and compliance evidence without hiring a five-person security team to run it. An SMB needs the same thing without a dedicated security hire at all. The best tool depends on which problem you have.

We built Fencer for SMBs and mid-market teams. If you're evaluating SAST tools and want to see the triage layer working on your own codebase, the benchmark data is public and Agent Scanner is available at github.com/Fencer-Security/agent-scanner.