{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Cyber Smokehouse","title":"Predictive Cyber Risk - Tim and Suzanne O’Neil - Cyber Smokehouse - Episode #22","html":"","width":"100%","height":180,"duration":3330,"description":"Most security programs are built around understanding what has already happened, but what if organizations could begin anticipating cyber threats before they materialize? In this episode of Cyber Smokehouse, Ernie Anderson and Graeme Payne welcome Tim and Suzanne O'Neil, founders of AigisPoint Predictive Intelligence. Drawing on decades of experience spanning enterprise security architecture, military leadership, entrepreneurship, and business strategy, they discuss their approach to predictive cyber risk, how AI and machine learning are reshaping threat modeling, and the realities of building an innovative cybersecurity startup.From balancing innovation with security to understanding AI's limitations, this conversation explores how organizations can begin thinking beyond reactive cybersecurity while remaining grounded in practical risk management. Takeaways:Traditional threat modeling remains largely static, creating an opportunity to apply AI and machine learning to forecast potential cyber threats before they emerge rather than relying solely on historical attack data.Publicly available sources, including industry reports, breach investigations, and threat intelligence, contain valuable information that can be combined with modern analytical techniques to identify emerging trends instead of simply documenting the past.Building innovative cybersecurity products requires leaders to constantly balance investment decisions, innovation, and acceptable business risk, recognizing that organizations cannot fund every initiative simultaneously.Early-stage cybersecurity companies face the challenge of proving value through customer adoption while simultaneously developing secure, production-ready platforms and meeting investor expectations.AI should be viewed as an enabling technology, not an infallible decision-maker. Human oversight remains essential because AI systems can still produce flawed outcomes and require validation before being trusted in security-critical...","thumbnail_url":"https://img.transistorcdn.com/OzVByYrVZ7pJIeb4cJ2-aoOkjC_j5Q1oz9lj1NhJqsk/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS80OTlh/YTdiNmUxMDU5OWY1/NWM4NTAxODM1NGNm/YTBiZi5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}