Semgrep vs Github Advanced Security

Switch from Github to Semgrep and give developers 20+ hours back per review cycle.

Book a demo Try for free
Developers:
20+ Hours
reclaimed per review cycle
Security Engineers:
70
false positives avoided per 100 findings

Precise AppSec that speaks your language, not the other way around

Automations that developers love

CodeQL and Dependabot generate too many false positives, making automation impossible without negatively impacting developers.

Semgrep's accuracy, simple policies, and PR experience make automations something that developers love, not hate.

Secure on day one, not day 365

CodeQL struggles with per-language complexity, build-step headaches, and CI/CD slowdowns, making it hard to scale across an organization.

Semgrep just works - quickly, across 40+ languages, and any number of repos.

AI-powered analysis, not just autofix

An autofix for a false positive is just noise at scale.

Semgrep uses AI to filter out false positives, flag breaking changes in upgrades, and so much more.

Smarter, not noisier

Teams using Semgrep see a 20% reduction in triage workload compared to pure static analyzers like CodeQL.

Semgrep Supply Chain's dataflow reachability analysis reduces false positives by 98% compared to Dependabot.

Semgrep
Github Advanced Security

Coverage, scale, and operations

  • Semgrep supports 40+ languages. With Semgrep Managed Scanning, teams can scan 100,000+ repos on day one.
  • Managed scanning circumvents all of the technical and organizational challenges that come with scaling, and saves thousands in CI costs.
  • CodeQL supports 10 languages and struggles with per-language complexity, build-step headaches, and CI/CD costs and slowdowns.
  • CodeQL requires compilation and must run in CI, resulting in long scan times that block developers and annoy platform teams.

Accuracy (SCA)

  • Reduce false positives by up to 98% with dataflow reachability analysis, available for 10+ languages.
  • Github's partner SCA vendors are an additional cost, and only perform function-level reachability analysis.
  • Dependabot lacks any form of reachability analysis.
  • Other Supply Chain solutions are an additional cost, and don't offer dataflow reachability analysis.

Accuracy (SAST)

  • Semgrep combines deterministic static analysis with contextual analysis (powered by AI), eliminating false positives right out of the box.
  • As teams triage findings, Semgrep learns and codifies security-relevant context to prevent the same issue from occurring again (internal services that are safe to ignore, mitigating functions, etc).
  • Without extensive manual customization, CodeQL generates an overwhelming number of false positives.
  • CodeQL's customization requires users to understand and write complex queries in a domain-specific language, with idiosyncrasies across different programming languages.

Secrets scanning

  • Semgrep uses semantic analysis, entropy analysis, and validation to flag hardcoded secrets while minimizing noise.
  • Semgrep uses static analysis and AI to surface generic secrets like passwords without flagging every randomized string in your codebase.
  • Github supports secrets scanning, but lacks validation for detected secrets.
  • Github does not support generic secrets detection.

Prioritization and remediation (SCA)

  • Dataflow reachability analysis and EPSS filtering make it easy to prioritize supply chain findings
  • Automatically create PRs for upgrades and patches. Breaking change analysis tells developers if an upgrade is safe to merge immediately, or helps them understand what needs to be changed.
  • Inundation from false positives wastes time and erodes developer confidence
  • AppSec teams must manually filter and prioritize findings

Prioritization and remediation (SAST)

  • Semgrep makes it possible to only show developers issues that are true positives, with an included fix tailored to their environment.
  • Semgrep's AI powered remediation experience gives developers a one-click fix in their PRs, with tailored explanations that help them validate and feel confident committing the change
  • Semgrep's automations and policies make it easy for security teams to get granular control over what issues developers see and where they see them (Jira ticket or PR comment)
  • Inundation from false positives wastes time and erodes developer confidence
  • AppSec teams must triage, validate, and assign issues to developers, or automate and risk flooding them with false positives
Coverage, scale, and operations
  • Semgrep supports 40+ languages. With Semgrep Managed Scanning, teams can scan 100,000+ repos on day one.
  • Managed scanning circumvents all of the technical and organizational challenges that come with scaling, and saves thousands in CI costs.
  • CodeQL supports 10 languages and struggles with per-language complexity, build-step headaches, and CI/CD costs and slowdowns.
  • CodeQL requires compilation and must run in CI, resulting in long scan times that block developers and annoy platform teams.
Accuracy (SCA)
  • Reduce false positives by up to 98% with dataflow reachability analysis, available for 10+ languages.
  • Github's partner SCA vendors are an additional cost, and only perform function-level reachability analysis.
  • Dependabot lacks any form of reachability analysis.
  • Other Supply Chain solutions are an additional cost, and don't offer dataflow reachability analysis.
Accuracy (SAST)
  • Semgrep combines deterministic static analysis with contextual analysis (powered by AI), eliminating false positives right out of the box.
  • As teams triage findings, Semgrep learns and codifies security-relevant context to prevent the same issue from occurring again (internal services that are safe to ignore, mitigating functions, etc).
  • Without extensive manual customization, CodeQL generates an overwhelming number of false positives.
  • CodeQL's customization requires users to understand and write complex queries in a domain-specific language, with idiosyncrasies across different programming languages.
Secrets scanning
  • Semgrep uses semantic analysis, entropy analysis, and validation to flag hardcoded secrets while minimizing noise.
  • Semgrep uses static analysis and AI to surface generic secrets like passwords without flagging every randomized string in your codebase.
  • Github supports secrets scanning, but lacks validation for detected secrets.
  • Github does not support generic secrets detection.
Prioritization and remediation (SCA)
  • Dataflow reachability analysis and EPSS filtering make it easy to prioritize supply chain findings
  • Automatically create PRs for upgrades and patches. Breaking change analysis tells developers if an upgrade is safe to merge immediately, or helps them understand what needs to be changed.
  • Inundation from false positives wastes time and erodes developer confidence
  • AppSec teams must manually filter and prioritize findings
Prioritization and remediation (SAST)
  • Semgrep makes it possible to only show developers issues that are true positives, with an included fix tailored to their environment.
  • Semgrep's AI powered remediation experience gives developers a one-click fix in their PRs, with tailored explanations that help them validate and feel confident committing the change
  • Semgrep's automations and policies make it easy for security teams to get granular control over what issues developers see and where they see them (Jira ticket or PR comment)
  • Inundation from false positives wastes time and erodes developer confidence
  • AppSec teams must triage, validate, and assign issues to developers, or automate and risk flooding them with false positives
Coverage, scale, and operations
  • Semgrep supports 40+ languages. With Semgrep Managed Scanning, teams can scan 100,000+ repos on day one.
  • Managed scanning circumvents all of the technical and organizational challenges that come with scaling, and saves thousands in CI costs.
  • CodeQL supports 10 languages and struggles with per-language complexity, build-step headaches, and CI/CD costs and slowdowns.
  • CodeQL requires compilation and must run in CI, resulting in long scan times that block developers and annoy platform teams.
Accuracy (SCA)
  • Reduce false positives by up to 98% with dataflow reachability analysis, available for 10+ languages.
  • Github's partner SCA vendors are an additional cost, and only perform function-level reachability analysis.
  • Dependabot lacks any form of reachability analysis.
  • Other Supply Chain solutions are an additional cost, and don't offer dataflow reachability analysis.
Accuracy (SAST)
  • Semgrep combines deterministic static analysis with contextual analysis (powered by AI), eliminating false positives right out of the box.
  • As teams triage findings, Semgrep learns and codifies security-relevant context to prevent the same issue from occurring again (internal services that are safe to ignore, mitigating functions, etc).
  • Without extensive manual customization, CodeQL generates an overwhelming number of false positives.
  • CodeQL's customization requires users to understand and write complex queries in a domain-specific language, with idiosyncrasies across different programming languages.
Secrets scanning
  • Semgrep uses semantic analysis, entropy analysis, and validation to flag hardcoded secrets while minimizing noise.
  • Semgrep uses static analysis and AI to surface generic secrets like passwords without flagging every randomized string in your codebase.
  • Github supports secrets scanning, but lacks validation for detected secrets.
  • Github does not support generic secrets detection.
Prioritization and remediation (SCA)
  • Dataflow reachability analysis and EPSS filtering make it easy to prioritize supply chain findings
  • Automatically create PRs for upgrades and patches. Breaking change analysis tells developers if an upgrade is safe to merge immediately, or helps them understand what needs to be changed.
  • Inundation from false positives wastes time and erodes developer confidence
  • AppSec teams must manually filter and prioritize findings
Prioritization and remediation (SAST)
  • Semgrep makes it possible to only show developers issues that are true positives, with an included fix tailored to their environment.
  • Semgrep's AI powered remediation experience gives developers a one-click fix in their PRs, with tailored explanations that help them validate and feel confident committing the change
  • Semgrep's automations and policies make it easy for security teams to get granular control over what issues developers see and where they see them (Jira ticket or PR comment)
  • Inundation from false positives wastes time and erodes developer confidence
  • AppSec teams must triage, validate, and assign issues to developers, or automate and risk flooding them with false positives

Semgrep vs. Dependabot in the wild

Time required to review findings:
Semgrep: 2.5 hours
Dependabot: 17.5 hours

Dependabot generates excessive noise for development teams, and burdens already time-strapped AppSec teams with extra work to verify vulnerabilities.

Semgrep's dataflow reachability analysis dramatically reduces false positives, as confirmed by Doyensec research.

Comparison also includes Snyk Open Source.

Grab the benchmark

Experience AppSec that's smarter, not noisier

Leading engineering teams use Semgrep to secure their code earlier in development, without impact to developer velocity.

Your privacy matters to us. By submitting this form, you agree to our Privacy Policy
or
Start for free