Actionable Oversight for Enterprise AI: Governing Models, Data, and Risk

AI is being deployed faster than most security teams can govern it. Whether it’s shadow AI models running without approval or sensitive data flowing into third-party tools without scrutiny, the result is the same: heightened regulatory risk and operational blind spots. Most organizations don’t have a clear system for AI oversight, leaving models and the data that fuels them outside the lines of compliance and control. This session unpacks how to bring structure to the chaos. From cataloging every AI model and its data inputs to enforcing usage policies and surfacing accountability metrics, CISOs can – and must – take the lead in governing enterprise AI. Key Takeaways ● Catalog Models and Data: Build visibility across all AI models in use, including third-party and shadow tools, and map their data sources. ● Set Clear AI Usage Policies: Define what’s allowed, what’s not, and what needs review – ensuring alignment across security, legal, compliance, and the business. ● Align with Governance Requirements: Ensure your AI programs meet regulatory, ethical, and corporate mandates before external frameworks mandate it. ● Monitor and Report: Put oversight in motion with active monitoring, risk scoring, and audit-ready reporting across your AI footprint. ● Get Ahead of Regulation: With global regulatory frameworks still catching up, internal governance is your best defense – and offense – for safe, scalable AI adoption.