Securing Research Infrastructure and Managing Shadow AI with Kevin Mortimer

This episode explores the technical hurdles of protecting academic research environments and navigating the shift to automated cloud architectures, drawing on ⁠Kevin Mortimer⁠'s twenty five years of technical leadership experience. The dialogue focuses on how higher education institutions face escalating threat profiles, moving from initial denial of service events to targeted supply chain compromises aimed at extracting student records. Kevin details how his team rapidly deployed mandatory multi factor authentication overnight and altered storage topologies by isolating valuable research data inside protected cloud vaults.

The discussion pivots to the operational reality of managing generative artificial intelligence across distributed campus networks. Kevin breaks down the friction between supporting early stage vibe coding for rapid proof of concept deployment and preventing shadow AI data exposure. He highlights the engineering required to build agent to agent communication platforms where firewall alerts automatically interface with backup systems to trigger live mounts and dynamic network segmentation.

In addition to addressing autonomous agent architectures, Kevin challenges the prevalence of vendor AI washing, emphasizing the need for technical leaders to scrutinize underlying mathematical models and prepare for shifting OpEx financial models driven by tokenization.

What You'll Learn

• Core strategies for securing academic research data within isolated cloud topologies.
• Methodologies for implementing emergency multi factor authentication policies across large user bases.
• Identifying supply chain vulnerabilities in third party student data record providers.
• Engineering autonomous agent to agent communication models between firewalls and recovery platforms.
• Frameworks for governing shadow AI usage and evaluating Model Context Protocol platforms.
• Utilizing vibe coding techniques for rapid scaffolding and proof of concept application development.
• Evaluating vendor transparency regarding underlying mathematical models to eliminate artificial intelligence washing.