Depthfirst: AI-Native Application Security for Engineering Teams
Depthfirst is an AI-native application security platform built for engineering and security teams that need to do more than manage alerts. It belongs to the emerging category of autonomous security platforms, combining static code analysis, supply chain scanning, secrets detection, and dynamic runtime testing into a single continuous workflow.
The platform is developed by an applied AI lab that trains its own vulnerability discovery models using reinforcement learning, which distinguishes it from tools that layer generative AI on top of existing rule-based engines. For SMEs operating in industries with high security exposure, such as fintech, infrastructure, and developer tooling, depthfirst addresses a growing structural problem: the volume of software being shipped is outpacing the capacity of human security teams to review it, and AI-powered attacks are accelerating faster than human-scaled defenses can respond.
AgentAya Verdict
Depthfirst stands out for reasoning about vulnerabilities the way a security researcher would: tracing data flows across services, connecting low-severity findings into exploitable attack chains, and verifying that fixes hold at runtime, not just in code. It is not a plug-and-play solution, and the value it delivers scales with the complexity of the codebase and the depth of its integration into the development workflow.
It is best suited to technically mature SMEs and growth-stage startups where a single undetected vulnerability carries serious business risk. Early adopters include Ripple, Chainguard, Supabase, Moveworks, Persona, and AngelList, all organizations with demanding security requirements and strong engineering teams. It is not a plug-and-play solution, and the value it delivers scales with the complexity of the codebase and the depth of its integration into the development workflow. A technically exceptional platform for security-conscious engineering teams
Score Breakdown
| Category | Score | Description |
| Features and functionality | 4.5 ⭐⭐⭐⭐⭐ | Four integrated modules covering the full application security lifecycle |
| Integrations | 4/5 ⭐⭐⭐⭐ | Native CI/CD support, API access, and connected system compatibility |
| Language and support | 3/5 ⭐⭐⭐ | Enterprise onboarding model; Spanish-language support not confirmed |
| Ease of use | 3.5 ⭐⭐⭐⭐ | Powerful but requires technical expertise to configure and interpret |
| Value for money | 0/5 | Pricing is not publicly listed; available by demo and custom quote |
AgentAya Overall Score: 3/5 ⭐⭐⭐
Ideal For
- Growth-stage startups and SMEs with engineering teams shipping code continuously
- Teams that have outgrown basic SAST tools and need runtime vulnerability verification
- Organizations with CI/CD pipelines looking to shift security left without adding manual review load
- Security teams that need to prioritize remediation based on proven exploitability, not CVSS scores alone
Not Ideal For
- Non-technical teams or companies without dedicated engineering and security staff
- Businesses with no CI/CD pipeline or version-controlled codebase
- Organizations looking for a lightweight, out-of-the-box security tool with minimal setup
Key Features
- Component Graph that maps data flows, cross-service relationships, and all dependencies across repositories
- Four specialized modules: Code scanning, Supply Chain analysis, Secrets and Sensitive Data detection, and Dynamic Testing
- Find, Validate, Fix, and Verify workflow that closes the loop from detection to confirmed remediation
- Deep scans covering the full repository including historical commits
- PR-level scanning that flags new issues before they reach production
- Security analytics dashboard tracking active vulnerabilities by repository and severity, burndown rates, and time to remediate
- Business context layer where teams describe each service in plain language to adjust risk scoring to their actual profile
- Natural language detection rules written in plain English and applied across the codebase
- API for querying findings, triggering scans, and integrating with existing developer tooling
These capabilities allow engineering teams to replace disconnected security tools with a unified, continuous workflow. Rather than receiving a periodic report of unranked alerts, teams get findings grounded in how the system actually behaves, complete with remediation guidance and runtime confirmation that fixes hold.
AI Features
Multiple large language models running in parallel to build structural understanding of each repository, spending hours per codebase on initial analysis
- Proprietary vulnerability discovery models: trained via reinforcement learning on security-specific tasks, not fine-tuned generalist models
- Credential detection by context: the system understands what a value does in the code, how it authenticates, and what access it unlocks, rather than matching against known patterns or formats
- Attack chain surfacing: connects findings across services to identify exploitable vulnerability combinations that isolated scanners would miss
- Continuous learning from developer feedback: with adjustments visible on the platform so teams can track what the system has learned over time
- Dynamic testing agents: that authenticate, navigate user interfaces and APIs, and chain requests to execute real attack paths against running applications
The genuinely intelligent layer here is the Component Graph combined with the reinforcement-learning-trained models. Standard SAST tools flag isolated issues in individual files. depthfirst reasons across the full system to determine whether a finding is exploitable given the actual architecture, permissions, and data flows of the application. The dynamic testing module then proves or disproves it against the live environment.
Integrations
- Source code repositories: via Connected Systems
- CI/CD pipelines: with PR scanning and deep scan triggers
- Ticketing systems: connected via API
- Third-party cloud environments: supported for Dynamic Tester, provided the customer has obtained consent from the cloud provider
- External API: for programmatic access to findings, scan triggers, and custom workflow integration
Teams with specific connector requirements should verify availability during the demo process. depthfirst includes an API, allowing technically capable teams to build custom integrations into their existing security and engineering tooling.
Security and Data Compliance
Customer data ownership is explicitly addressed in the Terms and Conditions. Customers retain ownership of all Customer Materials, and depthfirst assigns all rights to Customer Output back to the customer. Customer data is not used to train AI models unless the customer explicitly consents. The only exception is the Customer Instance, which is a version of the platform trained and optimized exclusively on that customer’s own data.
depthfirst holds SOC 2 Type II certification, verified through Vanta. A Trust Center is available by request, and an NDA may be required to access full compliance documentation.
The tool reserves the right to delete Customer Materials, Output, and related code within 30 days of contract termination. Customers are responsible for downloading their data before that window closes.
Encryption protocols, specific data residency options, and compliance with regional regulations such as GDPR are not confirmed in publicly available documentation and should be verified with depthfirst directly during the evaluation process.
Language: Customer Support and Interface
Depthfirst is an English-language platform and Spanish-language support is not confirmed by the available information. Given the enterprise onboarding model, customer support is likely managed through dedicated account contacts rather than self-serve channels.
AI Language
Depthfirst’s AI operates on source code and system architecture rather than natural language. Detection and reasoning capabilities are not dependent on the human language in which code is written or documented, which means the core scanning functionality works regardless of the programming language or inline comments used.
Mobile Access
Depthfirst does not offer a dedicated mobile application. It is a developer and security workflow tool designed for use within CI/CD pipelines and a web-based interface, and mobile access is not a primary use case for this category of tool.
Support, Onboarding, and Account Management
This platform follows an enterprise onboarding model. Access begins with a demo request and is followed by per-repository configuration and an initial deep scan that the company describes as taking several hours to complete. The continuous learning feature means the system refines its recommendations over time as developers interact with its findings.
The Dynamic Tester module carries specific contractual obligations: customers must designate a qualified security professional to monitor testing in real time, configure system exclusions before testing begins, and maintain incident response procedures. This level of rigor is appropriate for the capability but adds meaningful setup overhead compared to passive scanning tools.
Product documentation, changelogs, and release notes do not appear to be publicly accessible. Detailed documentation is likely gated behind the platform login and available only to active customers. Teams evaluating the platform should request access to documentation during the demo process before committing.
Ease of Use/UX
Depthfirst prioritizes depth and accuracy over simplicity. Findings surface as PR comments with one click to view full context and remediation steps, integrating naturally into existing developer workflows. The security analytics dashboard consolidates visibility across all repositories in a single view, tracking active vulnerabilities, severity distribution, and remediation progress over time.
Given the depth of the platform’s capabilities, particularly the Dynamic Tester module, it is reasonable to expect that some technical background would be beneficial for initial setup and configuration. Teams with dedicated security engineers are likely to move through onboarding more efficiently, while those without that experience may need additional time to extract full value from the more advanced features.
Pricing and Plans
This tool does not publish pricing publicly. Pricing is structured around the number of Active Developers and the specific modules included in the subscription, based on what is outlined in the Terms and Conditions. Subscription fees are invoiced annually in advance. Fee changes require 60 days of notice before the start of the renewal term, giving customers time to evaluate and, if needed, exit before the new pricing takes effect.
Teams evaluating the platform should request a demo through depthfirst.com to discuss plans based on their codebase size, team composition, and module requirements.
Case Study
A fintech startup processing payments across several microservices integrated depthfirst after a manual penetration test surfaced a privilege escalation path that had been live in production for several months without detection. After connecting their repository and completing the initial deep scan, the security team discovered three credentials that had been committed to the codebase months earlier and never rotated, all of which were still active and authenticating against live services.
The platform provided rotation context for each exposed credential, showing exactly where each one was used across the codebase so the team could replace them safely without breaking dependent services. PR scanning then caught two new authorization issues before they reached production in the following months. The team estimated that the combination of historical scanning and continuous PR coverage significantly reduced their manual review load while improving confidence in their actual security coverage.
Depthfirst vs Alternatives
Depthfirst vs Pentera
| depthfirst | Pentera | |
| Category | AI-native application security | Exposure validation |
| Primary focus | Vulnerabilities in code, dependencies, and secrets | Exploitable exposures in production environments |
| Testing approach | Static analysis plus dynamic testing grounded in code context | Continuous adversarial emulation in live environments |
| AI approach | Proprietary RL-trained vulnerability discovery models | AI-driven attack progression and algorithmic attack logic |
| Developer workflow | CI/CD native; findings as PR comments | Security team workflows; CTEM lifecycle management |
| Remediation | Replays attacks after each fix; resolved only when exploitation fails | Validates exposure reduction through continued testing |
| Free Access | No confirmed free tier; demo required | Not publicly listed |
| Best for | Engineering teams securing code across the full development lifecycle | Security teams validating whether production defenses hold |
Depthfirst focuses on the development pipeline, Pentera on the resilience of the production environment. The choice depends on the problem being solved: securing what your team builds or validating whether your defenses hold today.
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FAQs
Is Depthfirst suitable for SMEs?
Depthfirst is well suited to technically mature SMEs and growth-stage startups, particularly those in fintech, infrastructure, or developer tooling where security exposure is high. It is not designed for non-technical teams or companies without a CI/CD pipeline and at least one person with security expertise on staff.
Does Depthfirst support Spanish?
The platform interface and AI inputs are in English. Spanish-language support is not confirmed in publicly available documentation.
What are the best alternatives to Depthfirst?
Pentera is a strong option for teams focused on validating the resilience of their production environment rather than code-level security. Teams looking for a lighter entry point into application security should also evaluate traditional SAST and DAST tools, keeping in mind that depthfirst differentiates itself through cross-service reasoning and runtime verification.
Does Depthfirst use my code to train its AI models?
No, according to the Terms and Conditions. Customer materials are not used to train AI models unless the customer explicitly consents. The Customer Instance is the only exception, and it is trained exclusively on that customer’s own data.
How does Depthfirst confirm that a fix actually works?
The platform replays the same attack path after a fix is merged. A vulnerability is marked as resolved only when the exploitation attempt fails against the running application, not when the code change is committed.