DryRun Security
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About
DryRun Security is an AI-native application security platform designed to replace or augment traditional static analysis tools with agentic intelligence. Unlike standard SAST tools that rely on rigid regex rules and pattern matching—often resulting in excessive false positives—DryRun utilizes a proprietary Contextual Security Analysis (CSA) engine. This engine examines data flow, application architecture, and change history to understand developer intent and business logic. By integrating directly into the development workflow via GitHub and GitLab, it provides near-instant feedback on pull requests, acting as a virtual AppSec engineer that coaches developers on writing secure code in real time. The platform operates through a suite of specialized agents, including the Code Review Agent for PRs and the DeepScan Agent for repository-wide audits. Its analysis is guided by the SLIDE model (Surface, Language, Intent, Design, Environment), which aggregates signals from across the codebase to prioritize vulnerabilities by actual exploitability rather than just theoretical risk. Features like Natural Language Code Policies (NLCPs) allow teams to translate internal documentation into enforceable security rules without writing complex scripts. Additionally, the tool provides clear remediation guidance and can be configured to block merges based on defined risk thresholds, ensuring that security remains a gate rather than an after-thought. DryRun Security is ideal for mid-sized to large engineering organizations, high-growth startups, and specialized security teams looking to scale their impact without increasing headcount. It supports a wide range of popular languages including Python, JavaScript, TypeScript, Ruby, Go, and Java, along with numerous modern frameworks. For security leaders, it offers codebase intelligence that summarizes security postures and generates audit-ready artifacts for compliance frameworks like SOC 2 and ISO 27001. By focusing on what actually matters through contextual reasoning, it helps teams move away from manual code reviews and noisy scanners toward a more automated, AI-first security program.
Pros & Cons
Analysis results are delivered in seconds for most pull requests.
Identifies logic flaws and broken auth that traditional pattern-matching scanners miss.
Supports a wide variety of frameworks including React, Django, and Spring Boot.
Installation takes less than five minutes for supported SCM environments.
Does not train public AI models on your private code data.
Currently supports only GitHub.com and GitLab SaaS environments.
Requires a scheduled 15-minute call with an expert to complete tailoring of the instance.
Repository-wide DeepScans take hours rather than seconds to complete.
Use Cases
AppSec Architects can scale security coverage across large engineering teams by deploying agents that provide instant guidance on every PR.
Development Teams can receive real-time security coaching and fix suggestions directly within their existing GitHub/GitLab workflow.
Startups can ship code at high speed while maintaining a strong security posture against OWASP Top 10 risks.
Compliance Managers can generate audit-ready artifacts for SOC 2 or ISO 27001 by automatically capturing security-relevant changes.
Platform
Task
Features
• audit-ready compliance reporting
• github and gitlab integration
• remediation fix suggestions
• real-time pr feedback
• natural language code policies
• slide risk prioritization
• deepscan repository audits
• contextual security analysis (csa)
FAQs
When should I use a DeepScan review instead of a PR review?
Use DeepScan for broader coverage when onboarding repos, preparing for audits, or after major refactors. While PR reviews check incremental changes, DeepScan analyzes the entire repository to find risks across files and historical paths.
How does the SLIDE model prioritize vulnerabilities?
It evaluates Surface, Language, Intent, Design, and Environment to calculate a transparent risk rating. This ensures teams focus on exploitable flaws based on architecture and data flow rather than generic pattern matches.
What programming languages are supported?
The platform is optimized for Ruby, JavaScript, TypeScript, Python, Java, PHP, Go, C#, and Scala. It also includes framework-specific support for tools like React, Django, Spring Boot, and Laravel.
Can DryRun block a pull request from merging?
Yes, you can set specific risk thresholds that fail checks and enforce branch protection. This allows for automated governance based on configurable approval rules per team or repository.
How is my code kept safe during analysis?
DryRun uses isolated, ephemeral workloads and private models to ensure data is not shared or used to train public AI systems. Repositories are not cloned or retained after analysis completes.
How do I create custom security policies?
The Custom Policy Agent uses an AI assistant to convert your existing documentation into Natural Language Code Policies. This enforces your specific rules on every PR without the need for custom scripts.
Pricing Plans
Custom
Unknown Price• Pricing based on team size
• Full repository DeepScans
• Custom Policy Agent
• Codebase Insight Agent
• Audit-ready reporting
• Dedicated AppSec support
Free Trial
Free Plan• Installation in under 5 minutes
• 15-minute expert setup call
• Contextual PR reviews
• GitHub/GitLab integration
Job Opportunities
There are currently no job postings for this AI tool.
Ratings & Reviews
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