Railo
Railo is a GitHub-native security-remediation platform that scans pull requests for supported vulnerabilities and opens deterministic, reviewable fix pull requests with verification evidence. Its stated goal is to clear security backlogs without asking developers to merge AI-generated patches on faith.
At a Glance
- Small and medium-sized software teams
- B2B SaaS companies using Python
- Organizations with application-security backlogs
- Enterprise engineering and security teams needing audit evidence and policy controls
- +1 more
AI Tools by Railo
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Railo
AI Security Scanner for GitHub
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Latest News
Railo launched on GitHub Marketplace as Railo Dev, offering deterministic pull-request security fixes and verification evidence.
Zarif Latif published “Introducing Railo: The Security Bot That Fixes Your Code, Not Just Finds Problems,” describing the product's rule-based remediation, supported vulnerability types and languages, and GitHub Action workflow.
Railo published its measured remediation result: 68% SSRF clearance, representing 248 of 363 CWE-918 findings across 22 repositories and 21,768 findings.
Railo published updated privacy, security/data-handling, and service-level materials, including paid-plan availability, 90-day metadata retention, and a 99.5% uptime target.
Products & Services
GitHub App and Marketplace product that scans changed code in pull requests, comments findings and status checks, and opens a separate fix PR when it can produce a deterministic remediation. The Marketplace listing describes the implementation as running through GitHub Actions, with no external API calls and no code leaving the user's environment.
A fixed-scope service for one authorized Python repository: a point-in-time scan, in-scope hardcoded-secret and SQL-injection findings, reviewable fix PRs, verification evidence, and one follow-up scan.
Market Position
Railo positions itself between vulnerability scanners and remediation automation: unlike Snyk, Semgrep, and similar scanners that primarily report findings, it produces reviewable fix PRs; unlike generic AI coding assistants, it uses deterministic rules and verification evidence rather than LLM-generated patches. Its GitHub-native, organization-priced model also contrasts with seat-priced alternatives, while its explicit limitations mean it targets known, mechanically remediable patterns rather than novel vulnerabilities or business-logic flaws.
Leadership
Founders
Zarif Latif
Founder of Railo and IWEBai; describes himself publicly as a software engineer and AI researcher. He is building autonomous DevSecOps that automatically fixes and formally verifies vulnerabilities.
Executive Team
Zarif Latif
Founder
Software engineer and AI researcher; founder of IWEBai and builder of Railo's autonomous DevSecOps product.
Founding Story
Zarif Latif says he started Railo after repeatedly seeing scanners identify the same mechanical issues—such as SQL injection—while teams spent weeks triaging and hand-writing small fixes. The initial vision was a Dependabot-like workflow for vulnerabilities in first-party code: use deterministic rewrite rules for remediation rather than probabilistic LLM-generated patches, and deliver every change as a reviewable pull request.
Business Model
Revenue Model
Organization-based SaaS subscriptions priced by repository and pull-request-scan quotas rather than per developer, plus a fixed-scope $1,500 Finding Closure Sprint and custom Enterprise agreements.
Pricing Tiers
3 repositories, 200 pull-request scans per month, detection on every pull request, warn-mode comments, and findings dashboard.
Free features plus automated fix pull requests, 10 repositories, 2,000 scans per month, email notifications, and CSV export.
Pro features plus organization-wide policy controls, 50 repositories, 10,000 scans per month, Slack notifications, and priority support.
Unlimited repositories and scans, SARIF export, audit log, dedicated support, and the one-repository $1,500 Finding Closure Sprint.
Target Markets
- Small and medium-sized software teams
- B2B SaaS companies using Python
- Organizations with application-security backlogs
- Enterprise engineering and security teams needing audit evidence and policy controls
- Teams using GitHub pull-request workflows
- Clearing backlogs of known application-security findings
- Automatically remediating SQL injection, SSRF, path traversal, XSS, command injection, hardcoded secrets, and other supported patterns
- Security review and pull-request protection for development teams
- Preparing small B2B SaaS teams for customer security reviews, SOC 2 work, or a security-backlog deadline
- Enterprise audit evidence, organization-wide remediation policy, and controlled security operations across repositories