Corma raised $60M from Sequoia to build the defensive AI that cybersecurity is missing

In hundreds of simulations modelled on Fortune 500 companies with dozens of security tools, Corma tested leading AI models including GPT and Claude. First, the models attacked the simulated organisations and planted persistent threats. Then the same models were asked to defend and find what they had planted.

The attackers succeeded in 88% of simulations. The defenders caught 12%. The same AI that is increasingly capable of sophisticated attacks is poorly equipped to stop them. Corma raised $60 million in a seed round led by Sequoia Capital, with Khosla Ventures and Coatue, to build the defensive model the industry does not have.

The company, founded in 2025 and headquartered in Tel Aviv and San Francisco, is building a foundation model from the ground up for cybersecurity defence. Rather than selling software, Corma deploys AI agents that operate across an organisation’s existing security tools and carry out tasks end to end.

“We don’t replace anyone and we are not a product,” CEO Alon Pluda told Calcalist. “We sell virtual human resources.” Each organisation determines how many it needs. In early deployments at Fortune 100 and Fortune 500 companies across healthcare, financial services, energy, and retail, Corma’s systems reduced threat response times by more than 94% and expanded security coverage 15x.

The team brings together frontier AI researchers from Google and DeepMind with cybersecurity specialists from Israel’s Unit 8200. OpenAI just paused work on its Astra model because it could not rule out that the system had reached “critical cybersecurity” capabilities, meaning it could find and exploit zero-day vulnerabilities without human help.

That is the offensive side accelerating. Corma is betting that the defensive side needs its own purpose-built model rather than hoping general-purpose AI will do both jobs.

The gap Corma identified is structural. Offensive cybersecurity leverages the same coding and reasoning capabilities that make frontier models powerful. Defensive cybersecurity requires processing enormous volumes of audit logs, identifying weak signals over long periods, and maintaining consistency across thousands of decisions, tasks that general-purpose models are not trained for.

AI models from OpenAI, Anthropic, and Meta have all escaped test environments and breached other organisations in recent weeks. The attackers are already autonomous. Corma’s argument is that the defenders need to be too.