Trustworthy AI depends on verified data integrity across every system and process. This session positions data reconciliation as the nerve center of trust, integrating data quality, risk management, and governance into one unified assurance cycle. Participants will see how reconciliation identifies inconsistencies at the seams between systems—where most risk actually lives—and turns that discovery into actionable governance control. The session explores how a data reconciliation and quality engine continuously validates completeness and consistency between operational and analytical platforms, ensuring transparency for auditors and confidence for executives. Attendees will learn how reconciliation supports regulatory compliance, strengthens AI reliability, and provides an evidence-based foundation for automated decision-making. Real-world frameworks will illustrate how to evolve from siloed data cleansing to systemic trust engineering.

What the Audience Will Learn:

1. The concept of reconciliation as an assurance control.
2. How cross-system consistency underpins AI reliability.
3. Frameworks for regulatory evidence and audit transparency.
4. Building a culture of “trust through validation.”
5. Practical roadmap for deploying reconciliation enterprise-wide.

Speaker: Steve Zagoudis, CEO, MetaGovernance

Steve Zagoudis is a governance architect and leading authority on Data Governance, Information Governance, and Data Risk Management, specializing in preparing organizations for AI adoption through strong data quality foundations. As founder and CEO of MetaGovernance®, he helps regulated enterprises build the trusted data environments required for responsible AI, regulatory compliance, and transparent risk oversight. With more than 25 years of advising global corporations and government-sponsored enterprises, Steve has extensive experience resolving data integrity failures, eliminating spreadsheet-driven reporting risks, and modernizing reconciliation processes. His methodology emphasizes automated controls, clear lineage, and evidence-based trust—critical components of scalable, defensible AI platforms. A frequent speaker and writer, Steve advances industry understanding of Data Governance, data quality, and AI-ready information ecosystems while helping organizations reduce operational, compliance, and reputational risk.