Depthfirst, an AI-native cybersecurity startup, has raised $80 million in a Series B funding round led by Meritech Capital, with participation from Forerunner Ventures, The House Fund, and existing investors including Accel, Box Group, Liquid 2 Ventures, Alt Capital, and Mantis VC. The latest funding brings the company’s total capital raised to $120 million, coming less than three months after its Series A round.
Founded in 2024 and based in San Francisco, Depthfirst operates as an applied AI lab focused on securing modern software systems. Its platform leverages AI agents to analyze codebases, infrastructure, and workflows, identifying vulnerabilities and delivering actionable fixes directly within developer environments.
Alongside the funding, the company introduced its first in-house security model, dfs-mini1, initially designed to secure cryptocurrency smart contracts. Built on an open-source base and trained in security-specific environments, the model has shown the ability to extend beyond smart contracts and address broader cybersecurity challenges.
Depthfirst’s broader strategy focuses on developing domain-specific AI models tailored to cybersecurity use cases, addressing the growing gap between rapid software development and the limitations of traditional security tools. Its system is designed to detect complex vulnerabilities that conventional solutions often miss while reducing false positives and improving efficiency for developers.
Highlighting the shift in the industry, co-founder and CEO Qasim Mithani said, “To win in security, companies will need to deploy security-specific models in products optimized for real security workflows.”
The company plans to use the newly raised capital to expand its AI research team, develop additional specialized security models across new domains, and accelerate enterprise adoption of its platform. As AI continues to reshape both software development and cyber threats, Depthfirst is positioning itself to build next-generation security solutions capable of operating at the same speed and scale.
