DryRun Security vs Wiz Code

Capability	DryRun Security	Wiz Code	Verdict
Detection Categories1
What this tool covers	ℹ SAST, PR Reviews, SCA, Secrets, IaC.	ℹ SAST (Public Preview since Dec 2025), SCA, Secrets, IaC (1,000+ rules), Container, DSPM-in-code, Malware. Ingests third-party DAST via ASPM.	—
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.	✗ Core detection is rule/pattern-based ("thousands of curated rules"); AI is a triage and remediation layer bolted on, not an AI-native code-analysis engine.	DryRun leads
Agentic / Multi-Agent System	✓ Code Review Agent, Custom Policy Agent, DeepScan Agent, Codebase Insight Agent + specialized sub-agents; AGENTS.md support (Linux Foundation)	~ Discrete agents (SAST triage agent, Green Agent for remediation, Red Agent for offensive testing); no orchestrated multi-agent code-review pipeline described.	DryRun leads
Natural Language Policies	✓ Natural Language Code Policies (NLCP); Policy Library with 16+ pre-built policies; Custom Policy Agent enforces on every PR	✗ Unified policy engine is platform/UI-driven; plain-language interaction exists only via the MCP server for querying, not for policy authoring.	DryRun leads
Model-Independent Verification	✓ Separates code generation from code verification; works regardless of which AI model or human generates code	✗ No model-independent verification layer described; AI features are triage and remediation outputs, not verified findings.	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	~ Relies on the cloud Security Graph enriched with code-to-cloud tracing (source → CI → registry → image); no separate code-semantic knowledge graph.	DryRun leads
Cross-repo Analysis	✓ Accumulates security knowledge across all repos in an org for org-wide pattern detection.	✗ No cross-repository code analysis stated; correlation happens at the cloud-asset level in the Security Graph, not across code repositories.	DryRun leads
Code Security Memory	✓ Persistent memory of FP fingerprints, dismissed findings, and accepted risk decisions across reviews.	✗ No code-security memory concept; Green Agent uses historical remediation patterns but this is not stated as a memory feature.	DryRun leads
Custom DryRun Cyber Models	✓ Purpose-built models trained for code security, not general-purpose foundation models.	✗ Not available	DryRun leads
Multi-model Architecture	✓ Routes each task to the best-fit model rather than relying on a single LLM.	✗ No public statement of a multi-model architecture; underlying models for AI features are not disclosed.	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.	✓ Real-time scanning and fixing of AI-generated code inside AI-native IDEs via plugins/skills, secure-rules-files for Claude/Cursor/Copilot, and block mode at file save / pre-commit / pre-push.	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	✓ AI-BOM auto-inventories AI coding tools across the org (LangChain, Gemini Code Assist, Copilot, Cursor).	Tie
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 specific detection of malicious AI-agent skills; closest capability is general malware scanning in the codebase.	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	✓ Wiz MCP Server (GA) translates plain-language queries into Wiz Code workflows, from issue discovery to pull-request creation. Listed on AWS Marketplace.	Tie
Continuous Enforcement Layer	✓ Runs on every PR automatically. Policies enforced at the merge gate. Always on — not invoked per-task.	~ Unified policy engine with developer guardrails (e.g., "zero critical issues in production") and CI/CD block mode across the SDLC; enforcement is one layer inside the broader CNAPP.	DryRun leads
Per-Review Economics (predictable vs. usage-metered)	✓ Flat subscription pricing based on team size. Usage pricing applies to DeepScan product.	✗ Sold as annual code-license SKU (AWS Marketplace lists ~$58,500 for 100 code licenses / 12 months); no per-review or per-PR pricing.	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.	✗ No product claim of business-logic flaw detection; findings are CWE- and rule-based with cloud-context prioritization.	DryRun leads
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	~ Continuous workload observation and remediation tracking via the Security Graph; no explicitly named code-specific baseline or trending feature.	DryRun leads
Workflow & Supply Chain2
SCM Support	✓ GitHub and GitLab (native apps with OAuth)	~ GitHub and GitLab confirmed (VCS connectors, CIS for GitHub/GitLab); Bitbucket and Azure DevOps not confirmed on product pages.	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.	✓ SBOM via SCA and AI-BOM covering AI-tool usage inventory across the org.	Tie
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.	~ Code findings map to OWASP ASVS, NIST SSDF, CIS Controls, OWASP Top 10 CI/CD, OpenSSF; platform-wide 300+ frameworks including SOC 2, PCI, NIST, HIPAA, FedRAMP. MISRA and STIG not confirmed.	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 for Wiz Code specifically (0/5) — g2.com/products/wiz-code/reviews. Reviews aggregate under the parent Wiz product.	—
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 dedicated Wiz Code G2 reviews. PeerSpot: "…appreciating its fast, accurate vulnerability detection. It integrates well into workflows." — Cyber Security Consultant, Enterprise (peerspot.com/products/wiz-code-reviews)	—
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 dedicated Wiz Code G2 reviews. PeerSpot: "I've used Wiz Code for three years… though I wish it better reflected business impact beyond just severity ratings." — Security Manager, Consultancy, Enterprise (peerspot.com/products/wiz-code-reviews)	—
Common G2 Complaint Themes	ℹ UI/portal speed; desire for more analyzer customization (g2.com/products/dryrun-security/reviews)	ℹ CVSS/severity-weighted findings without business-impact context; false-positive volume at scale (platform-wide); unclear ROI for the code product; high cost for smaller teams (peerspot.com/products/wiz-code-reviews)	—

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