Semgrep vs iProov
Side-by-side comparison of AI visibility scores, market position, and capabilities
Open-source static analysis for security vulnerabilities and code quality in dozens of languages; commercial Semgrep Code and Supply Chain products serve enterprise security teams at scale.
About
Semgrep is an application security company founded in 2020 that has raised over $100M and built one of the most widely adopted open-source static analysis tools in the developer security ecosystem. The platform allows security engineers and developers to write custom code analysis rules in a readable pattern-matching syntax that mirrors the code being analyzed, making it far more approachable than legacy SAST tools. Semgrep supports over 30 programming languages and integrates into developer workflows through IDE plugins, pre-commit hooks, and CI/CD pipelines. The company offers Semgrep Code for SAST, Semgrep Supply Chain for dependency vulnerability scanning, and Semgrep Secrets for detecting hardcoded credentials. Semgrep has been widely adopted at major technology companies for internal security rule development and is used by security teams to enforce coding standards at scale. The combination of open-source community adoption and an enterprise SaaS offering has made Semgrep a leading platform-of-record for developer-first application security.
iProov surpassed 1M daily verifications in 2025; first biometrics vendor certified to NIST 800-63-4; raised $85.1M total; serves U.S. DHS, UK Home Office, GovTech Singapore, ING, and UBS.
About
iProov is a London-based biometric identity verification company founded in 2011 by Andrew Bud. The company specializes in face biometric authentication with a focus on genuine presence assurance — verifying not just that a face matches an identity document, but that the person is physically present, alive, and not presenting a digital attack such as a deepfake, photo replay, or 3D mask. Its flagship technology, Genuine Presence Assurance (GPA), uses a proprietary illumination-based liveness detection approach that is highly resistant to injection attacks and synthetic media manipulation.
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