depthfirst

What depthfirst does

depthfirst provides an autonomous security platform that acts as an operating system for modern application security. It offers a comprehensive defensive security intelligence layer for applications, understanding code, infrastructure, and business logic. The platform uses AI to find and exploit vulnerabilities at scale, providing shared context, intelligent workflows, and autonomous remediation for humans and AI agents. It aims to protect, detect, validate, and remediate at AI scale, improving with continued use and reducing false positives. The system ingests continuous signals and deploys enterprise-grade security from design to production, securing every layer of a system from code to supply chain. It also includes governance, system of record, audit trails, and role-based access, and is compliant with rigorous security standards like SOC 2 and BYOK.

Products

• Dependency FirewallBlocks malicious packages and detects malicious behavior before it spreads through the environment.
• Security ReviewerValidates human and AI-generated code changes to prevent vulnerabilities, sensitive data, or malware from entering the codebase.
• Agentic PentestingConfirms exploitable vulnerabilities by testing running applications with real attack paths.
• Secrets & Sensitive DataDetects and validates credentials across codebase, CI/CD pipelines, and runtime environments.
• Supply ChainTraces risk through the full dependency tree and surfaces vulnerabilities with a real execution path.
• GovernanceAllows setting policies once, which are then inherited by every scan, fix, and agent action.
• System of recordCentralizes every finding, decision, and fix, making them searchable, exportable, and audit-ready.
• Audit trailLogs every human and agent action, making them immutable and traceable.
• Role-based accessProvides granular access control for teams, scoped by repository, environment, or organization.

Key capabilities

• Autonomous security from design to production
• Self-learning security system that improves with continued use
• Ingests continuous signals
• Enterprise-grade security
• Traces business logic, data flows, and cross-service interactions across codebase
• Detects malicious behavior
• Confirms exploitable vulnerabilities with real attack paths
• Detects and validates credentials
• Traces risk through full dependency tree
• Governance for policy setting
• System of record for findings and fixes
• Audit trails for human and agent actions
• Role-based access control
• SOC 2 compliant
• BYOK (Bring Your Own Key) support
• Integrated with existing workflows
• Maps entire application and builds structural understanding using LLMs
• Reasons through application to find real attack paths and business logic flaws
• Evaluates conditions for exploitation and runs dynamic tests
• Generates pull requests for confirmed vulnerabilities
• Replays attacks after fixes are merged to confirm resolution
• Continuous learning from developer feedback
• Component graph mapping data flows and cross-service relationships
• Security analytics for tracking vulnerabilities, burn down, and time to remediate
• Business context integration for risk profiling
• Natural language rules for detection
• API connectivity for programmatic integration

Use cases

• Securing software applications
• Vulnerability discovery and exploitation prevention
• Malware detection in dependencies
• Validating human and AI-generated code changes
• Detecting and validating credentials
• Tracing supply chain risk
• Automated security policy enforcement
• Auditing security findings and actions
• Managing access control for security teams
• Building AI-native security programs
• Increasing code-security coverage
• Securing production environments

AI approach

depthfirst uses AI, including multiple LLMs and reinforcement learning, to build a self-learning security system that understands application structure, identifies vulnerabilities, and automates remediation. It trains agents for vulnerability discovery and improves over time by learning from developer feedback.

Tech named: LLMs, Reinforcement Learning, AI agents

Industries served

• Computer and Network Security
• Retail
• Hospitals
• Banks
• Power grids
• Defense systems
• Fast-growing startups
• Global enterprises

What it says sets it apart

• Autonomous security from design to production
• Self-learning system that improves with continued use
• Over 90% fewer false positives than traditional tools
• Detects novel malware in dependencies in under 3.5 minutes
• Saves over 6 FTE developer years
• Over 50% cheaper than the next best security harness
• Uses multiple LLMs in parallel to build structural understanding of systems
• Reasons through applications to find real attack paths, business logic flaws, and chained vulnerabilities
• Generates pull requests for confirmed vulnerabilities against actual codebase and conventions
• Confirms vulnerability resolution by replaying attacks after fixes
• Backed by investors with history in security, infrastructure, and mission-critical software
• Founders have experience building and securing large-scale software systems at companies like Faire, Cash App, AWS, Databricks, and Google DeepMind

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.