Depthfirst Inc. has announced that it has raised $80 million in new funding to accelerate the development of its artificial intelligence-native security platform, as the company looks to address the growing complexity of modern cyber threats. The investment will be used to train advanced security models across new domains, expand its AI research team, and scale enterprise adoption of its platform.
Founded in 2024, depthfirst is tackling a rapidly evolving threat landscape where software development cycles are outpacing traditional security solutions. At the same time, cyber attackers are increasingly leveraging AI to identify and exploit vulnerabilities. The company’s mission is to secure software at the same speed and scale at which it is built and attacked.
At the core of its offering is the General Security Intelligence platform, which deploys custom AI agents to analyze codebases, infrastructure, and development workflows. By leveraging deep contextual understanding and machine learning, the platform is designed to detect subtle and complex vulnerabilities that conventional security tools often overlook.
The Series B funding round was led by Meritech Capital Partners, with participation from Forerunner Ventures, The House Fund, Accel, Box Group, Liquid 2 Ventures, Alt Capital, and Mantis VC. The announcement comes less than three months after the company raised $40 million in its Series A round, signaling strong investor confidence in AI-driven cybersecurity innovation.
Depthfirst co-founder and CEO Qasim Mithani highlighted the shifting dynamics of the security market, noting that AI is poised to disrupt legacy security stacks. He emphasized that success in this space will depend on deploying security-specific models tailored to real-world workflows rather than relying on general-purpose AI systems.
In conjunction with the funding announcement, depthfirst also introduced its first in-house security model, dfs-mini1. The model is initially focused on securing cryptocurrency smart contracts, marking a strategic step toward building specialized intelligence within the company’s broader platform.
Dfs-mini1 was developed using an open-source foundation model and further refined through reinforcement learning in security-specific environments. It has been evaluated using OpenAI’s EVMBench benchmark, which measures the effectiveness of models in identifying smart contract vulnerabilities.
According to the company, early testing shows that dfs-mini1 outperforms leading frontier models while operating at significantly lower costs – reportedly between 10 to 30 times more efficient. Internal assessments also suggest that the model can generalize beyond smart contracts, demonstrating strong performance across a range of security tasks.
Chief Technology Officer Andrea Michi emphasized the importance of owning the model training process, stating that it allows the company to optimize for domain-specific priorities such as vulnerability detection and verification. This approach enables depthfirst to deliver models that are not only more cost-effective but also better aligned with real-world security needs.
As AI continues to reshape the cybersecurity landscape, depthfirst’s latest funding and product developments position the company to play a key role in redefining how organizations secure modern software systems.
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