**Data vs. Goliath: Using Data to Hold Vendors Accountable**

When vendor promises don’t match operational reality, how do you prove it? In today’s security landscape, organizations rely heavily on third-party platforms, yet dashboards, telemetry, and reported outcomes don’t always align with what teams observe in practice.

This session explores how structured data collection and analysis can transform vendor disputes into measurable outcomes. Drawing from real-world experiences across cloud security, infrastructure, and vendor management, Megan Atkins Elliott shares practical techniques for identifying performance gaps, validating concerns, and building evidence-based cases for action.

Attendees will learn how to collect meaningful metrics, challenge vendor claims with objective data, communicate findings to leadership, and create accountability without damaging strategic partnerships. When trust is tested, data becomes your strongest ally.

Speaker bio:

Megan Elliot | Director, Cloud Security Engineering; Elliot Security Intelligence Consulting

Megan Atkins Elliott is a cloud security leader specializing in transforming complex technical data into strategic business outcomes. As Director of Cloud Security Engineering, she develops data-driven frameworks that enhance cloud visibility, strengthen vendor accountability, and provide meaningful risk insights to executive leadership.

With more than a decade of experience spanning education, finance, and insurance, Megan brings a unique perspective shaped by backgrounds in infrastructure engineering, security architecture, and forensic science. Her work focuses on using data to drive operational excellence, improve decision-making, and bridge the gap between technical teams and business stakeholders.

A CISSP-certified security professional, Megan is recognized for building collaborative, evidence-based security programs that create accountability, transparency, and measurable results. Outside of her professional pursuits, she is a dedicated fitness enthusiast and automot