Manage episode 522498540 series 3701347
Tom and Patrick sit down with Christine Fignar, Cybersecurity Analyst at the Federal Reserve Bank of Minneapolis, to unpack one of the most misunderstood areas of security: insider threats and human-driven risk. Christine’s background spanning aviation, HR, communications, and counterterrorism gives her a rare perspective on stress, behavior patterns, and the subtle signals that often precede incidents.
We get into her “Cone of Uncertainty” framework for visualizing how threats form and evolve, discuss hiring fraud, offboarding gaps, nation-state recruitment, and why organizations routinely miss early warning signs. A conversation for anyone looking to better understand, track, and communicate human-centric risk.
🔍 Topics Covered
- How the Federal Reserve approaches insider threat detection
- The “Cone of Uncertainty” model — and why it works for cyber
- Why human stress is one of the strongest predictors of insider risk
- How to watch for early “storm signals” inside your organization
- The rise of nation-state hiring scams (North Korea, China)
- OSINT techniques for tracking behavioral risk
- Why cyber teams must become “English-to-English translators”
- Problems with spreadsheets, CVSS scores, and risk communication
- The hidden dangers in onboarding/offboarding workflows
- Real-world examples: sales data theft, disgruntled employees, access misconfigurations
- Why organizations still fail at protecting sensitive information
🎙️ About Our Guest
Christine Fignar
Cybersecurity Analyst, Federal Reserve Bank of Minneapolis
Specializes in insider threat, incident response, human-behavior-driven risk, and threat intelligence analysis.
Background includes aviation operations, HR, communications, and counterterrorism/anti-corruption studies.
📢 If You’re in Cybersecurity, This Episode Is For You
Perfect for security leaders, threat intel analysts, defenders, SOC teams, and anyone who wants to understand the human side of modern cyber threats — beyond the dashboards and detection tools.
6 episodes