Head-to-Head Comparison
DryRun Security vs GitHub Advanced Security
Start with the requirements a change must meet before it can merge. Compare GitHub’s documented controls with DryRun’s automated adversarial review and application context. DryRun evaluates customer-defined policies, returns repair guidance to supported coding agents and uses configured required checks to enforce merge decisions.
Native GitHub Security — GitHub-native SAST + SCA + Secrets via CodeQL, Dependabot, and Secret Scanning
17
DryRun Leads
4
Tie
0
GHAS Leads
21
Capabilities Compared
| Capability | DryRun Security | GHAS | 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 |
✗ Single engine (CodeQL) |
DryRun leads |
| Natural Language Policies | ✓ | ✗ Policies configured via CodeQL query packs and YAML workflow files, not natural language. |
DryRun leads |
| Model-Independent Verification | ✓ DryRun security evaluation is separate from developer or supported coding-agent repair. Changed code receives fresh review. Source |
✗ CodeQL output is the final finding; Copilot Autofix does not verify independently. |
DryRun leads |
| Code Security Knowledge Graph | ✓ PR reviews and DeepScan both build and use the DryRun Security Context Graph. |
✗ No knowledge graph; CodeQL databases are per-repo snapshots. |
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 | ✓ | ✗ — |
DryRun leads |
| 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 |
~ GitHub documents CodeQL source-code analysis within supported languages and query coverage. Source |
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 |
✗ Security Overview shows CodeQL alerts but no AI-code-specific visibility. |
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 detection for malicious AI agent skills or plugins. |
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 |
✗ GHAS has no MCP server. |
DryRun leads |
| 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. |
~ | DryRun leads |
| Code Security Intelligence3 | |||
| Business Logic Flaw Detection | ✓ Investigates authorization and business-logic vulnerabilities such as IDOR and tenant-isolation failures. Source |
✗ Limited to CodeQL query coverage |
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 |
~ Security Overview tracks detection and remediation trends across accessible repositories' default branches. Source |
DryRun leads |
| Workflow & Supply Chain2 | |||
| SCM Support | ✓ GitHub and GitLab (native apps with OAuth) |
✓ | Tie |
| SBOM / AI-BOM Generation | ✓ | ✓ GitHub documents dependency SBOM generation in SPDX JSON. Source |
Tie |
| Reporting & Compliance1 | |||
| Compliance Framework Mapping (SOC 2, PCI, NIST, OWASP, MISRA, STIG) | ~ | ~ CodeQL suites mapped to CWE; SOC 2 / compliance reporting lighter. |
Tie |
| Market Feedback (G2)4 | |||
| G2 Rating / Review Count | ℹ 4.9/5 (19 reviews) — g2.com/products/dryrun-security/reviews |
ℹ GitHub platform reviews are not a GHAS-specific rating. 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 GHAS-specific praise is cited here. Source |
— |
| 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 GHAS-specific criticism is cited here. Source |
— |
| Common G2 Complaint Themes | ℹ UI/portal speed; desire for more analyzer customization (g2.com/products/dryrun-security/reviews) |
ℹ No GHAS-specific complaint themes are cited here. Source |
— |
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