Episode Details

Title: The Right Model for the Right Security Task | A Brand Highlight Conversation with Michael Roytman, Co-Founder and CTO of Empirical Security

In this Brand Highlight conversation, Michael Roytman, Co-Founder and CTO of Empirical Security, joins Sean Martin to discuss why choosing the right AI model for the right task is essential for effective cybersecurity.

Michael Roytman explains how Empirical Security takes a data-driven, Moneyball-style approach to preventative security. The company builds and maintains an ensemble of models, including the open EPSS model used by over 100 vendors, global models for vulnerability exploitation forecasting, and local models tailored to each customer's unique environment.

The conversation explores a critical finding: LLMs perform poorly at predictive security tasks. Michael Roytman shares research he published in Forbes comparing EPSS to LLMs from Google, OpenAI, and Anthropic. While LLMs excel at summarization and classification, they struggle to predict future exploitation events. Purpose-built models like XGBoost consistently outperform LLMs for probability forecasting.

Empirical Security positions itself as a data science company operating on security data rather than a traditional security vendor. With two-thirds of the founding team holding data science backgrounds, the company trains models from scratch and continuously retrains them as environments and threat landscapes evolve.

This is a Brand Highlight. A Brand Highlight is a ~5 minute introductory conversation designed to put a spotlight on the guest and their company. Learn more: https://www.studioc60.com/creation#highlight

GUEST

Michael Roytman, Co-Founder and CTO of Empirical Security

On LinkedIn | https://www.linkedin.com/in/michael-roytman/

RESOURCES

Learn more about Empirical Security | https://www.empiricalsecurity.com

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KEYWORDS

Empirical Security, Michael Roytman, data-driven security, vulnerability management, EPSS, risk-based vulnerability management, AI in cybersecurity, machine learning security, LLM limitations, predictive security models, XGBoost, local models, global models, preventative security, Moneyball security, cybersecurity AI, threat intelligence, security data science, model retraining, ITSPmagazine, Brand Highlight, Studio C60

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