CrowdStrike and Meta just dropped something pretty big for the cybersecurity and AI world: CyberSOCEval. If you’ve ever wondered how to actually measure whether large language models (LLMs) can do more than just spit out clever text and instead hold their ground in a high-pressure SOC (security operations center), this is the toolkit meant to give answers. It’s built on Meta’s existing CyberSecEval framework but now supercharged with CrowdStrike’s frontline adversary intel. The whole idea is to move past the hype and put AI through real-world drills — incident response, malware analysis, threat comprehension — the things that actually keep defenders up at night.
The problem is straightforward: SOCs are drowning in alerts, attackers evolve daily, and security teams don’t have the bandwidth to chase every lead. Everyone knows AI could help, but knowing which models really deliver under stress is another story. Until now, there hasn’t been a consistent benchmark. That’s where CyberSOCEval comes in. Think of it like a stress test for LLMs, but instead of load-balancing servers, it’s about simulating adversary tactics and watching how well the AI holds up. It’s adversary tradecraft versus machine reasoning, distilled into open benchmarks that anyone can use.
What makes this interesting is the openness. Meta wants the security and AI communities to take the suite, iterate, and improve it. CrowdStrike brings the credibility of “we’ve seen these attacks in the wild” so the scenarios aren’t academic fluff but built from real incidents. Put them together, and you’ve got a North Star for developers and security teams alike. Devs can refine their models based on meaningful feedback, and SOC managers can decide where AI is worth deploying versus where humans still need to stay in the loop.
Both companies made a point to stress the bigger picture. For Meta, it’s about showing that open source AI isn’t just theoretical but can serve frontline defenders against AI-driven threats themselves. For CrowdStrike, it’s about setting the bar for how AI-native security should actually work in practice. The vibe here is less “look at our shiny partnership” and more “we need to stop guessing which models work — let’s standardize and raise the floor.”
CyberSOCEval is live now and open source, which means the security community has a new sandbox to test what really matters: whether AI can keep pace with real attackers instead of just talking about them. This feels like one of those rare industry moves where the marketing lines up with the actual need. AI in the SOC just got a measuring stick.
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