Veracode Solutions for the Public Sector

Securing Applications in the Age of AI: Veracode vs. Synk

When it comes to accuracy, governance, remediation, and effectively managing application risk, they fall short. With higher false positives, you end up addressing the wrong issues while real vulnerabilities might slip through. Accountability is also a major concern; developers can overlook findings, leaving AppSec teams uninformed. So, ask yourself: do you want just a scanning tool or an integrated platform that covers everything from code to cloud?

Capabilities Comparison

Point Solutions or Holistic Platform

Veracode: Delivers an integrated platform that scans applications from code to cloud, seamlessly connecting development and security teams.

Snyk: Scans before deployment using SAST and SCA but lacks native scanning capabilities for production environments.

Developer-Friendly AppSec Program

Veracode: Integrates with the tools developers use, helping organizations build comprehensive AppSec programs that reduce risk through robust policies and reporting, leveraging extensive expertise in building thousands of such programs.

Snyk: Lacks the scale for full AppSec programs, offering limited policies and reporting. Additionally, Snyk allows developers to ignore findings, which can leave security teams uninformed.

IDE Integrations

Veracode: Streamlines the scanning and securing of code with popular IDE plugins for IntelliJ, Android Studio, PyCharm, Eclipse, VS Code, and Visual Studio.

Snyk: Claims to offer 12 IDE integrations, but 9 of them are variations of a single JetBrains plugin.

Coverage of Languages and Frameworks

Veracode: Offers market-leading coverage with support for over 30 languages and 100 frameworks.

Snyk: Supports less than half the languages and frameworks that Veracode does.

Quality Results and Remediations

Veracode: Provide findings with the lowest false positive rate out of the box, requiring minimal tuning. Utilizes AI for scalable and speedy fixes, backed by proprietary security research to avoid vulnerabilities from manipulated or poisoned AI models.

Snyk: Detection and remediation are affected by high false positive rates and fewer detectable flaw types, leading to noisy findings.