Head-to-Head Comparison
DryRun Security vs Corgea
Decide how your team will move from a security finding to a reviewed repair. Compare DryRun with Corgea on remediation and review workflows. DryRun supplies findings and application context to supported coding agents, then applies automated adversarial review to the resulting code changes.
AI-based code security analysis and repair workflows. Module availability varies by plan. Source 1 · Source 2 · Source 3 · Source 4
13
DryRun Leads
8
Tie
0
Corgea Leads
21
Capabilities Compared
| Capability | DryRun Security | Corgea | Verdict |
|---|---|---|---|
| Detection Categories1 | |||
| What this tool covers | ℹ | ℹ | — |
| AI & Intelligence9 | |||
| AI-Native Architecture | ✓ | ✓ | Tie |
| Agentic / Multi-Agent System | ✓ PR review and repository investigations use specialized security agents and evaluations. Source |
~ Corgea Agent supports investigations and scan workflows; multi-agent orchestration is not established. Source |
DryRun leads |
| Natural Language Policies | ✓ | ~ PolicyIQ accepts plain-English instructions and business context for detection, triage and fixes when enabled. Policy testing applies to BLAST and false-positive policies, not every policy type. Source |
DryRun leads |
| Model-Independent Verification | ✓ DryRun security evaluation is separate from developer or supported coding-agent repair. Changed code receives fresh review. Source |
✗ Beagle LLM + AST runs as a single pipeline; no separate verification. |
DryRun leads |
| Code Security Knowledge Graph | ✓ PR reviews and DeepScan both build and use the DryRun Security Context Graph. |
✓ Call graph with CodeIQ enrichment |
Tie |
| 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 | ✓ | ✓ Beagle, vendor-trained model for code security |
Tie |
| Multi-model Architecture | ✓ Routes each task to the best-fit model rather than relying on a single LLM. |
✗ — |
DryRun leads |
| AI Coding Agent Security6 | |||
| Securing AI-Generated Code | ✓ Reviews human- and AI-written code in connected, configured workflows. Source |
~ Scans AI-generated code; no dedicated AI-code-review features. |
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 |
✗ Findings dashboard only; no AI-coding observability. |
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 |
✗ No dedicated AI-agent 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 integration confirmed on marketing pages |
Tie |
| Continuous Enforcement Layer | ✓ Configured PR findings fail checks; required source-control protections enforce the customer’s merge policy. Source |
✓ | Tie |
| 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 |
~ BLAST module for broken auth, IDOR, transaction tampering |
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 |
~ Dashboards with scan history |
DryRun leads |
| Workflow & Supply Chain2 | |||
| SCM Support | ✓ GitHub and GitLab (native apps with OAuth) |
✓ GitHub, GitLab, Azure DevOps, Bitbucket |
Tie |
| 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) | ~ | ~ OWASP / CWE mapping. |
Tie |
| Market Feedback (G2)4 | |||
| G2 Rating / Review Count | ℹ 4.9/5 (19 reviews) — g2.com/products/dryrun-security/reviews |
ℹ No G2 reviews — g2.com/products/corgea/reviews |
— |
| 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 |
— |
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