Cybersecurity startup Empirical Security Inc. announced today that it has raised $12 million in new funding to develop and deploy custom artificial intelligence cybersecurity models tailored to each organization’s unique infrastructure and threat landscape.

Founded earlier this year, Empirical Security aims to help organizations in defending against threats through custom-built AI models. The company pitches itself as moving away from the traditional one-size-fits-all approach to threat detection by building tailored solutions for each customer environment.

Empirical’s platform offers dual-model architecture that combines the power of global threat intelligence with localized, organization-specific insights. The models are trained on about 2 million daily exploitation events sourced from internet-scale datasets, while local models are fine-tuned using customer-provided and curated internal data. That, the company claims, delivers highly accurate threat prioritization based on the specific context of the organization.

The approach is designed to allow cybersecurity teams to make faster, evidence-based decisions, backed by predictive models that highlight the most critical vulnerabilities. The idea is that instead of relying on generic risk scores that may not reflect the actual danger to a specific business, the company’s local models provide actionable intelligence customized to a given company’s operational environment.

Empirical also emphasizes explainability and decision support as key elements of its platform. The platform gives security leaders the ability to justify their strategies with data by integrating risk-based analysis with measurable prediction outputs. The transparency is especially valuable in boardroom discussions, compliance reporting and budgeting, where clear articulation of cybersecurity priorities is essential.

“Today’s cyberattacks are custom-built using AI and your own infrastructure against you,” said co-founder and Chief Executive Michael Roytman. “Defending with generic, one-size-fits-all models is a start, but only custom, localized models — trained on your data and environment — can close that gap.”

Costanoa Ventures led the seed round, with participation from DNX Ventures, Sixty Degree Capital, HPA Holdings and a number of individual investors.

Image: SiliconANGLE/Reve