As enterprises accelerate the deployment of autonomous systems, securing these technologies has become a critical priority across the cybertech ecosystem. General Analysis raises $10M in seed funding to address emerging risks tied to agentic AI, signaling growing investor confidence in a new category of security infrastructure. The company announced that the round was led by Altos Ventures, with participation from 645 Ventures, Menlo Ventures, Y Combinator, and a group of strategic investors and angels. Founded by researchers with experience at NVIDIA, Cohere, and DeepMind, the company is focused on closing a widening gap in AI security as organizations integrate autonomous agents into real world workflows.
The urgency of the problem is underscored by recent internal testing. In March, General Analysis deployed an adversarial agent that successfully manipulated 50 live customer service AI systems into distributing more than $10 million in fabricated benefits within minutes. Out of 55 systems tested, only five resisted the attack. These findings highlight the vulnerabilities present in current deployments and form the basis of the company’s approach to stress testing enterprise systems before they go live.
The company was founded by Rez Havaei, formerly of Cohere and NVIDIA, alongside Maximilian Li and Rex Liu. Together, they argue that agentic AI introduces fundamentally new challenges that cannot be addressed using traditional cybersecurity frameworks. Unlike conventional software, these systems behave unpredictably, making vulnerabilities difficult to detect through code inspection alone.
This challenge is already evident across industries such as finance and customer support, where AI agents are increasingly embedded into high impact operations. Organizations often face a tradeoff between usability and security, with limited tools available to properly evaluate risk. “We hear from security teams that they want agents that are secure by design,” said Rez Havaei, CEO of General Analysis. “What that often turns into in practice is a stack of isolation layers and ad hoc context restrictions that makes a system feel more controlled. Those measures either fail to eliminate the underlying vulnerability or constrain the agent enough to limit its usefulness. The problem is that feeling safer and being safer are not the same thing.”
The company’s approach centers on empirical testing under adversarial conditions. “Our position is that security for AI systems is an empirical problem. It has to be grounded in rigorous measurement of how those systems behave under realistic and adversarial conditions. You cannot prove an agent is safe,” said Maximilian Li, co-founder of General Analysis. “You can only measure how often it fails, and how badly, and drive both numbers down.”
General Analysis combines adversarial evaluations with layered defenses to help enterprises identify failure points and optimize system configurations. According to co founder Rex Liu, “One advantage of agents is that they are much easier to study systematically than the human workflows they are beginning to replace. Many of those workflows were never especially secure to begin with, and their failures are often hard to observe or improve rigorously. But as those workflows become agentic, they also become more measurable and more improvable which creates a path for businesses to become more secure in practice than they were before.”
Investors see this as a defining moment for the sector. “Agentic systems represent a paradigm shift in security. Safety and security in the AI era demand continuous adversarial testing rooted in deep research, not static rule sets,” said Tae Yoon, Partner at Altos Ventures. “Rez, Rex, and Max are exactly the kind of team this moment calls for: technically brilliant, deeply scrappy, and moving incredibly fast. We’re proud to lead this round and partner with them from the earliest days.”
As General Analysis raises $10M in seed funding and expands its enterprise footprint, the company’s work reflects a broader shift in cybersecurity strategy. With agentic AI moving rapidly into production environments, the ability to measure, test, and harden these systems will play a defining role in shaping the next generation of secure digital infrastructure.
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