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Some problems yield to partial fixes. Cybersecurity does not. Locked-down endpoints do not matter if a user still clicks the phishing link. Careful data classification and tight access control do not matter if the identity provider gets abused. The chain snaps at whichever link is thinnest, and adversaries only need one.
That is why security is a breadth-first problem. Every layer has to hold at once, even though every layer evolves at a different pace. Endpoints, identity, cloud posture, SaaS, and network telemetry each move on their own roadmap, driven by their own vendors, their own release cycles, and their own threat models. The result is the ecosystem practitioners live with today: fragmented, rapidly changing, and famously hard to reason about as a single system.
A typical enterprise runs 45 different security tools to protect an average of 1061 applications. Every combination demands its own product knowledge, its own tuning muscle memory, and its own set of best practices. That is a near-impossible expectation even for the best-funded teams, and it explains why so much of the modern SOC runs on tribal knowledge, one-off scripts, and dozens of open browser tabs.
Recent advances in large language models change what is possible. A cohesive layer of AI agents can now read across products, ingest fresh threat advisories, and reason about enterprise events with a fluency no single analyst can match. That vision sits at the edge of what today's models can do, but the direction is unmistakable, and it is the foundation for AI-driven security done properly.
Attackers are not waiting. Automated cyber espionage, convincing phishing content, and LLM-generated malware all lower the cost of attack. If defense stays manual while offense goes autonomous, the economics tilt against defenders every quarter.
Defense cannot cost more than attack. That is not a slogan; it is a budget line. AI has to carry the same fraction of the defender's workload it already carries for the attacker, or the gap widens.
Today we are launching Simbian, a mission-driven company, with a $10M+ over-subscribed funding round led by Cota Capital, Icon Ventures, Firebolt, Secure Octane, and Rain Capital, and supported by more than 15 of the most successful business leaders of our time.
Simbian is building a Fully Autonomous Security platform for enterprises. Fully autonomous, in our definition, means humans make every strategic decision while AI executes on them. Today, security operators burn most of their day on product integrations, low-level APIs, triaging low-value events, and finishing one-off tasks. Little energy is left for the calls only they can make.
Picture a car whose transmission is so complex the driver spends the entire trip shifting gears and never steers. That is the modern SOC. Simbian flips it. You steer; AI handles the rest.
Fully autonomous does not mean unattended. Simbian is self-improving, not self-driving. Humans keep containment authority and every escalation call, and the agents learn from the outcomes of the decisions humans make.
LLMs bring risks a security platform cannot ignore. A hallucinating model might block the wrong port, uninstall a production service, or open a fresh security hole while trying to close another. Threat advisories can carry hidden prompts designed to hijack the model. Security operators handle sensitive data that must never leak into training pipelines or third-party inference.
That is why we built TrustedLLM, the first LLM system engineered end to end for safety and reliability in security automation. TrustedLLM combines patent-pending technical innovations, a proprietary corpus built from a crowd-sourced intelligence game, and original research into hallucination containment. The design goal is simple: the model refuses cleanly when it does not know, acts correctly when it does, and leaves an audit trail for the analyst either way.
The easiest way to picture the shift is to compare a normal triage queue before and after.
The change is not that the analyst disappears. The change is that the analyst spends the day on judgment calls, tuning, and hunting, instead of tab-switching and copy-pasting between consoles. That is the practical shape of AI-driven security when it actually works.
A journey of a thousand miles begins with a single step. The path we have chosen, Fully Autonomous Security, is the only one we see that keeps organizations safe in the years ahead. We picked it not because it is hard (though the hard parts are the fun ones) but because our collective safety depends on it.
We're working with a small group of forward-leaning organizations while we prove the platform in production. If that sounds like you, contact us.
