AI Risk in Practice. Scenarios, Accountability, and What Boards Expect

AI risk is no longer theoretical. It’s operational, measurable, and under board-level scrutiny.

In this session, RadarFirst’s Jared Combs and cybersecurity leader Chris Hetner break down real-world AI and data risk scenarios, from large-scale breaches to vendor failures, and what separates well-managed outcomes from avoidable ones.

In this session, we’ll explore:

• Real-world AI and data risk scenarios, including “good, bad, and avoidable” outcomes
• What happens when basic controls fail, including MFA gaps, weak encryption, and vendor-related issues, and how those failures escalate into regulatory and reputational risk
• How breach probability, threat exposure, and business impact should shape board-level conversations
• What changes when you’re managing high data volumes, operating as a public company, or facing investor scrutiny
• Why accountability remains with your organization, even when incidents originate with third parties
• Practical governance and control best practices to prevent avoidable failures
• How to strengthen vendor oversight and incident response readiness
• How to translate these scenarios into clear, credible conversations that drive action at the executive and board level