GeneralAnalysis

Our Mission

We build the security layer for agentic systems. We are based in San Francisco.

The Security Gap

Autonomous agents invert traditional security assumptions and force new tradeoffs:

•Agents need broad, flexible access to tools, APIs, and data. That requirement clashes with least-privilege security models and pushes safety to the margins without a new paradigm.
•Governance-based approaches and general alignment training by labs are insufficient to make applications secure. Models are not aligned to a specific set of enterprise risks, so security must be active, adversarial, and continuous.
•AI systems have nonlinear and nondeterministic attack surfaces where interaction effects dominate. A component can look safe in isolation and still create exploit paths when combined with other agents, tools, and workflows.

With faster deployment loops and multi-agent workflows, security teams are forced into triage. We focus on system-level risk and realistic exploit paths instead of isolated robustness checks.

What We've Built

We train proprietary adversarial agents that digests a customer's runtime environment, maps the full graph of tools and permissions, and generates multi-step exploits that target the seams between components. This approach has greatly outperforms baseline red-teaming techniques on academic robustness benchmarks and powers our security evaluation platform. Our offensive capabilities allow us to forecast vulnerabilities during CI/CD and implement state-of-the-art defenses.

RED TEAM

Automated Red-Teaming

General Analysis applies a context-aware attacker model that maps tool and permission graphs, then generates multi-step exploit chains that expose system-level risks across interacting agents.

BLUE TEAM

Strategic Blue-Teaming

Our blue team distills red-team findings into vulnerability forecasting and runtime controls, enabling CI/CD security evaluation today and endpoint protection for AI agents next.

World-Class Research

Our founding team brings together exceptional talent from world-class institutions and quantitative trading firms and deep knowledge and expertise in cybersecurity and AI research.

Learn More

Whether you're interested in our products, research, or joining our team, we'd love to hear from you.

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The Security Gap

Autonomous agents invert traditional security assumptions and force new tradeoffs:

•Agents need broad, flexible access to tools, APIs, and data. That requirement clashes with least-privilege security models and pushes safety to the margins without a new paradigm.
•Governance-based approaches and general alignment training by labs are insufficient to make applications secure. Models are not aligned to a specific set of enterprise risks, so security must be active, adversarial, and continuous.
•AI systems have nonlinear and nondeterministic attack surfaces where interaction effects dominate. A component can look safe in isolation and still create exploit paths when combined with other agents, tools, and workflows.

With faster deployment loops and multi-agent workflows, security teams are forced into triage. We focus on system-level risk and realistic exploit paths instead of isolated robustness checks.

What We've Built