Head-to-Head Comparison
AI-native Codebase Scanner — AI-native codebase security scanner built into Claude Code, limited research preview (Feb 2026)
| Capability | DryRun Security | Claude Code | Verdict |
|---|---|---|---|
| Detection Categories1 | |||
| What this tool covers | ℹ SAST, PR Reviews, SCA, Secrets, IaC. |
ℹ Partial: SAST, Secrets. |
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| AI & Intelligence9 | |||
| AI-Native Architecture | ✓ Built AI-native in 2023. Model-independent verification layer. Uses a fleet of specialized agents (Code Review, DeepScan, Custom Policy, Codebase Insight) rather than one general model. |
✓ Fully AI-native (it is the AI). Good for exploratory review inside a Claude session. Invoked per-task by a developer; not a continuous enforcement layer. |
Tie |
| Agentic / Multi-Agent System | ✓ Code Review Agent, Custom Policy Agent, DeepScan Agent, Codebase Insight Agent + specialized sub-agents; AGENTS.md support (Linux Foundation) |
~ Multi-stage prompt pipeline — Anthropic describes Code Review as multi-agent (parallel agents for logic/security/regression, then an aggregator). Closer to multi-agent than single-model. (<a href="https://emelia.io/hub/claude-code-review-test" target="_blank" rel="noopener noreferrer">Emelia</a>) |
DryRun leads |
| Natural Language Policies | ✓ Natural Language Code Policies (NLCP); Policy Library with 16+ pre-built policies; Custom Policy Agent enforces on every PR |
~ CLAUDE.md and security-scan-rules.txt give policy-like behavior per-session. Not a merge-gate policy engine. |
DryRun leads |
| Model-Independent Verification | ✓ Separates code generation from code verification; works regardless of which AI model or human generates code |
✗ (tied to Claude Opus) |
DryRun leads |
| Code Security Knowledge Graph | ✓ Accumulates organizational knowledge across PRs; cross-repo intelligence; learns risk tolerance from dismissal patterns (nitpicks, FPs, accepted risks); FP fingerprinting improves decision quality over time |
✗ Reasoning is per-session; no persistent cross-PR or cross-repo knowledge graph. |
DryRun leads |
| Cross-repo Analysis | ✓ Accumulates security knowledge across all repos in an org for org-wide pattern detection. |
✗ — |
DryRun leads |
| Code Security Memory | ✓ Persistent memory of FP fingerprints, dismissed findings, and accepted risk decisions across reviews. |
✗ — |
DryRun leads |
| Custom DryRun Cyber Models | ✓ Purpose-built models trained for code security, not general-purpose foundation 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 every PR regardless of who whether the author was human or AI, DryRun is especially effective at identifying the types of flaws AI coding tools are prone to create. |
✓ Claude Code Review (March 2026) is purpose-built for PR review: parallel agents for logic/security/regression, then aggregation. Anthropic reports 84% finding rate on large PRs in internal testing (<a href="https://emelia.io/hub/claude-code-review-test" target="_blank" rel="noopener noreferrer">source</a>). Teams + Enterprise only. |
Tie |
| 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 |
~ Scan results with severity; not an observability 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 |
✗ No dedicated detection for malicious AI agent skills (e.g., skills.sh-style 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 |
✓ Native MCP support (Anthropic's own protocol) |
Tie |
| Continuous Enforcement Layer | ✓ Runs on every PR automatically. Policies enforced at the merge gate. Always on — not invoked per-task. |
✗ Invoked per-task by a developer inside a Claude session. Code Review can be wired to PRs, but developer workflow and usage-based cost make it selective rather than universal (see Per-Review Economics row). |
DryRun leads |
| Per-Review Economics (predictable vs. usage-metered) | ✓ Flat subscription pricing based on team size. Usage pricing applies to DeepScan product. |
✗ $15–$25 per PR (token-metered). Large PRs can exceed $25. Estimated ~$40K/mo for a 100-dev team at 1 PR/day (<a href="https://emelia.io/hub/claude-code-review-test" target="_blank" rel="noopener noreferrer">Emelia, Mar 2026</a>). Enterprise plans only. |
DryRun leads |
| Code Security Intelligence3 | |||
| Business Logic Flaw Detection | ✓ Covers all logic flaws, BOLA, IDOR, broken auth, multi-tenant isolation, mass assignment, privilege escalation, OAuth, WebSocket auth bypass, and more. See docs.dryrun.security for more. |
✓ LLM reasoning is well-suited to logic flaws. |
Tie |
| Git Behavioral Analysis | ✓ Git Behavioral Graphs: code churn, temporal coupling, knowledge decay, temporal anomalies, intent mining |
✗ 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 with findings over time (limited preview) |
DryRun leads |
| Workflow & Supply Chain2 | |||
| SCM Support | ✓ GitHub and GitLab (native apps with OAuth) |
~ GitHub only for PR reviews and CI/CD. |
DryRun leads |
| SBOM / AI-BOM Generation | ✓ DeepScan generates SBOM with SCA agent providing dependency inventory and license checking (Dependency License Check policy). Plus AI-BOM and runtime context — not just a static dependency graph. |
✗ No SBOM or AI-BOM generation capability. |
DryRun leads |
| Reporting & Compliance1 | |||
| Compliance Framework Mapping (SOC 2, PCI, NIST, OWASP, MISRA, STIG) | ~ Findings tagged with OWASP Top 10 and CWE. Dedicated SOC 2 / PCI / STIG report templates are on the roadmap, not shipped. |
✗ Not a compliance product. |
DryRun leads |
| Market Feedback (G2)4 | |||
| G2 Rating / Review Count | ℹ 4.9/5 (19 reviews) — g2.com/products/dryrun-security/reviews |
ℹ 4.8/5 (25 reviews) — g2.com/products/anthropic-claude-code/reviews |
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| 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 "Powerful AI coding assistant" — praised for general coding ability, security scanning too new for dedicated praise (g2.com/products/anthropic-claude-code/reviews) |
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| 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 "My tokens keep on running out and legacy models like opus 4.5 and sonnet 4.5 are not accessible anymore." (g2.com/products/anthropic-claude-code/reviews) |
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| Common G2 Complaint Themes | ℹ UI/portal speed; desire for more analyzer customization (g2.com/products/dryrun-security/reviews) |
ℹ Token consumption; CLI less polished than competitors; occasional inaccurate output (g2.com/products/anthropic-claude-code/reviews) |
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