Head-to-Head Comparison
DryRun Security vs Corridor
Follow a code change from your coding agent through review and repair. Compare DryRun with Corridor across the relevant workflows and source-control integrations. DryRun supplies automated adversarial review, retained application context and configured policy checks, while developers or supported coding agents make the fixes.
Security review for code changes and coding-agent guardrails; feature scope varies by plan and SCM. Source 1 · Source 2 · Source 3
17
DryRun Leads
3
Tie
1
Corridor Leads
21
Capabilities Compared
| Capability | DryRun Security | Corridor | Verdict |
|---|---|---|---|
| Detection Categories1 | |||
| What this tool covers | ℹ | ℹ | — |
| AI & Intelligence9 | |||
| AI-Native Architecture | ✓ | ~ | DryRun leads |
| Agentic / Multi-Agent System | ✓ PR review and repository investigations use specialized security agents and evaluations. Source |
~ | DryRun leads |
| Natural Language Policies | ✓ | ~ Plain-English rules for AI coding guardrails; scope is coding tools, not full AppSec policy |
DryRun leads |
| Model-Independent Verification | ✓ DryRun security evaluation is separate from developer or supported coding-agent repair. Changed code receives fresh review. Source |
✗ IDE-time guardrail only; no post-generation verification layer. |
DryRun leads |
| Code Security Knowledge Graph | ✓ PR reviews and DeepScan both build and use the DryRun Security Context Graph. |
✗ IDE-scoped; no organization-wide code security graph. |
DryRun leads |
| Cross-repo Analysis | ✓ DryRun looks locally first and, when needed, automatically investigates relevant context, such as authorization logic, across repositories it has permission to access. |
✗ — |
DryRun leads |
| Code Security Memory | ✓ Retained application context and feedback inform later investigations; changed code receives fresh review. Scanner-excluded candidates and human dismissals are distinct records. Source |
✗ — |
DryRun leads |
| Custom DryRun Cyber Models | ✓ | ✗ Routes across frontier models (no custom-trained security model) |
DryRun leads |
| Multi-model Architecture | ✓ Routes each task to the best-fit model rather than relying on a single LLM. |
✓ Switches across frontier models (Cursor/Claude Code/etc.) per task |
Tie |
| AI Coding Agent Security6 | |||
| Securing AI-Generated Code | ✓ Reviews human- and AI-written code in connected, configured workflows. Source |
~ Real-time guardrails at the point of AI code generation; narrow to AI-tool scenarios. |
DryRun leads |
| AI Coding Visibility / Observability | ✓ Code Insights with AI Assistance (beta): NL queries for risk, trends, exposure; org-wide visibility; per-repo drill-down; file-level security history |
~ Dashboard for AI coding activity; narrow to their guardrails product |
DryRun leads |
| Malicious AI Agent Skill Detection | ✓ Policy Library includes Malicious AI Agent Skills Detection: flags skills/plugins that could enable data theft, backdoors, or code execution |
~ Monitors AI agent behavior for policy violations; not dedicated skill scanning |
DryRun leads |
| MCP Integration | ✓ DryRun Insights MCP server: security summaries, PR analysis, trend monitoring, file-level history; connects via Direct HTTP, Claude Shortcuts, or mcp-remote |
✓ MCP server is core to the product |
Tie |
| Continuous Enforcement Layer | ✓ Configured PR findings fail checks; required source-control protections enforce the customer’s merge policy. Source |
~ Configured GitHub severity policy can fail a status check; branch protection must require it to block merge. GitLab merge-policy enforcement is documented as forthcoming. Source |
DryRun leads |
| Per-Review Economics (predictable vs. usage-metered) | ✓ Flat subscription pricing based on team size. Usage pricing applies to DeepScan product. |
✓ Subscription pricing. |
Tie |
| Code Security Intelligence3 | |||
| Business Logic Flaw Detection | ✓ Investigates authorization and business-logic vulnerabilities such as IDOR and tenant-isolation failures. Source |
~ | DryRun leads |
| Git Behavioral Analysis | ✓ DeepScan uses Git Behavioral Graphs for churn, temporal coupling, knowledge decay, anomalies and intent analysis. Source |
✗ Not available |
DryRun leads |
| Continuous Baseline & Risk Trending | ✓ Risk Register with Critical/High/Medium/Low severity; AI Assistance for Insights with NL queries, trend monitoring, and 30-day window analysis |
✗ Dashboard focus is on IDE guardrail usage; no repo-level risk trending. |
DryRun leads |
| Workflow & Supply Chain2 | |||
| SCM Support | ✓ GitHub and GitLab (native apps with OAuth) |
~ | DryRun leads |
| SBOM / AI-BOM Generation | ✓ | ✗ No SBOM or AI-BOM generation capability. |
DryRun leads |
| Reporting & Compliance1 | |||
| Compliance Framework Mapping (SOC 2, PCI, NIST, OWASP, MISRA, STIG) | ~ | ✓ Maps AI coding policy to enterprise compliance frameworks. |
Competitor leads |
| Market Feedback (G2)4 | |||
| G2 Rating / Review Count | ℹ 4.9/5 (19 reviews) — g2.com/products/dryrun-security/reviews |
ℹ No customer star rating was shown on the Corridor G2 listing in the November 1, 2026 UTC source observation. Source |
— |
| Notable G2 Praise (Attributed) | ℹ "DryRun goes far beyond what rule-based SAST tools offer. It catches things other tools completely miss — like middleware that's defined but never mounted, or trust boundary misalignments." — Jabez A., Director, Product Security Architecture, Enterprise (g2.com/products/dryrun-security/reviews) |
ℹ No G2 reviews available |
— |
| Notable G2 Criticisms (Attributed) | ℹ "I do somewhat wish there were more customization options for tuning the analyzers, but that seems to be in the works." — Kyle R. (g2.com/products/dryrun-security/reviews) |
ℹ No G2 reviews available |
— |
| Common G2 Complaint Themes | ℹ UI/portal speed; desire for more analyzer customization (g2.com/products/dryrun-security/reviews) |
ℹ Too new for G2 feedback |
— |
Ready to see DryRun Security in action?
Get a personalized demo and see how DryRun compares on your codebase.
Get a Demo