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Kosta
Georgopoulos
VP, Information Technology
Middlesex Federal Savings
Kosta Georgopoulos is Vice President of Information Technology at Middlesex Federal Savings, F.A., and a technology and risk executive with extensive expertise in information security, IT governance, and regulatory compliance. His career has focused on the intersection of technology strategy, cybersecurity, operational excellence, and enterprise oversight, aligning innovation with business priorities and evolving regulatory expectations. With over a decade of leadership experience, Kosta has established a strong record of advancing cybersecurity maturity, enabling secure digital transformation, and strengthening organizational capabilities. His leadership is characterized by strategic foresight, operational discipline, and a commitment to sound governance, fostering confidence among executive stakeholders, Boards, and regulatory authorities. Kosta has directed enterprise technology initiatives, developed comprehensive security and control frameworks, and led programs spanning incident response, operational resilience, and third-party risk. His broader professional experience includes engagements with leading global organizations across multiple industries, providing a valuable perspective on complex business environments, technology innovation, and organizational transformation. As a trusted advisor to executive leadership and Boards, Kosta translates complex technology challenges and emerging cyber risks into strategic insights that inform decision- making.
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29 October 2026 12:00 - 12:30
Panel | Securing the AI stack: Hardening models, agents, and data pipelines
Security teams are being asked to protect AI systems that are already in production, often before the controls to match exist. Two CISOs, an IT audit officer and a VP of IT discuss how they harden models, agents and data pipelines without becoming the function that says no. This panel will cover how access control, logging and monitoring apply to models and agents, how third-party model risk gets assessed, and what audit teams and boards expect to see as evidence. Key Takeaways: → Where to apply access control, audit logging and monitoring across models, agents and data pipelines → How to assess third-party model risk and enforce AI policy without blocking delivery → What a defensible incident response plan looks like when an AI system is the thing that fails