DryRun Security
AI-native code security verification that validates exploitable code risk and guides remediation on every pull request.
By DryRun Security · 4.9/5 Value-Position score (estimate)
Positioning guardrails
Best for
- AppSec teams drowning in legacy SAST noise who need context-aware, exploitability-validated findings inside pull requests.
- Engineering organizations using AI coding assistants that want independent verification of AI-generated and human-written code.
- GitHub- or GitLab-centric development teams that want security feedback delivered where developers already work.
- Security leaders who need continuous policy enforcement across repositories without maintaining brittle regex rule sets.
- Regulated teams (for example PCI-scoped commerce) that need evidence of systematic, repeatable code review.
Ideal size: 20–1,000 engineers people · Startup to enterprise with a Git PR workflow and an AppSec owner
Not for
- Buyers who want one platform covering cloud, container and runtime security (CNAPP/CSPM); DryRun focuses on code and pull requests.
- Organizations without a Git-based pull-request workflow to hook into.
- Companies with no in-house development or AppSec owner who can act on findings.
- Teams expecting fully autonomous fixes applied without developer or AppSec review.
Value metrics scorecard
Time-to-Value
Same day (install in minutes)
~1 days to first production value
Total Cost of Ownership
On request
Not published; demo-led enterprise pricing with no public price list or self-serve tier
Implementation Friction
2/5
Engineering + admin effort required
Value-Position score
out of 5 · model estimate
Full cost breakdown
Mandatory implementation fee
None
Seat tiers
Not published
Add-on costs
- None
Company & support
Who is behind DryRun Security, and how your team gets help once it is live.
Market position
Where DryRun Security sits against its closest alternatives. Pick any two of cost, speed, friction and buyer score, and up to 9 companies to compare.
Quadrant view
Typical annual cost × Time-to-value
The lines cross at the median of the solutions shown, so about half sit on each side of each line.
Companies on the chart 6 / 10
- DryRun Security
- Upwind
- Intruder
- SureCloud
- GitGuardian
- Vicarius vRx
Add or change companies
Up to 10 companies including DryRun Security. Listed closest first.
Stack fit signal
Compatibility with standard B2B ecosystems.
No supported MCP path today, so it cannot be driven from an AI client.
AI & MCP readiness
What DryRun Security ships in AI, and what it asks of your ecosystem.
AI features shipped
Marketed as an AI-native, agentic SAST: specialized agents trace input, logic, permissions and data flow to judge code intent and exploitability, and natural-language custom policies are enforced on every code change. No model provider, key-management model, or AI audit/observability detail is published on the pages reviewed.
Your data & models
- Trains on your data
- Not recorded — ask the vendor
- Runs on
- Not recorded
- AI pricing
- Not recorded
In your ecosystem
- AI connection
- Not supported
- Model key
- Not recorded
- AI usage audit
- Not recorded
Compliance attestations
* IAPP AIGP certifies individuals, not products. It means named staff hold the credential — not that the platform does.
Bottom line
DryRun Security is an AI-native code security (SAST) platform that reviews pull requests and full repositories. Its Contextual Security Analysis engine builds a knowledge graph of architecture, authorization and data flow, validates exploitability, and reports findings where developers work in GitHub, GitLab, Slack and AI coding agents. The vendor cites 500,000+ code reviews a week, 2x accuracy and 90% lower noise versus legacy SAST, a 4.9/5 G2 rating, and Fortune 50 customers. Pricing is demo-led and not published.
Frequently asked questions
What does DryRun Security do?
DryRun Security is an AI-native code security verification platform. It reviews pull requests and full repositories, maps how an application actually works (architecture, authorization boundaries, data flow), validates whether findings are truly exploitable, and guides developers and their AI coding agents to the fix. Findings, change summaries and natural-language custom policies are surfaced and enforced on every code change.
How is DryRun different from legacy SAST tools?
The vendor positions DryRun against pattern- and regex-based scanners: it claims roughly 2x greater accuracy in pull-request and repository review and about 90% lower noise by reasoning about exploitability and impact rather than the mere presence of a pattern, and says teams maintain zero rule sets because custom checks are written in natural language.
How quickly can a team get value?
Fast. A customer (Defect Dojo) says the app was up and running 'in just a couple of minutes', and DryRun requires no rule libraries to build or maintain. Setup is via SCM integration (GitHub or GitLab) with notifications in Slack. No implementation programme or implementation fee is published.
Does DryRun integrate with AI coding assistants or MCP?
Yes for assistants: Claude Code, Claude Desktop, Codex and Cursor are listed as integrations, and DryRun markets agent-native remediation. MCP appears in DryRun's own platform diagram and in a customer quote about 'MCP capabilities', but no official MCP server documentation was available in the pages reviewed, so buyers should confirm the current MCP surface directly with the vendor.
Is DryRun Security pricing published?
No. None of the pages reviewed publish list prices, seat tiers or add-on costs; the buying motion is demo-led (Get a Demo or Get a Free Security Assessment). Budget for an enterprise code-security line item and ask for per-developer or per-repository pricing when you scope a pilot.
Which languages and platforms are supported?
DryRun reads Python, JavaScript, TypeScript, Java, C#, Ruby and Golang (with 'and more' stated), and integrates with GitHub, GitLab, Slack, Claude Code, Claude Desktop, Codex and Cursor, plus an API. Reviews run in pull requests and as a full-repo DeepScan baseline scan.