Conference Description
Key Takeaways
- One-day, in-person conference addressing security challenges introduced by enterprise AI adoption
- Covers AI security strategy, data protection, governance, and emerging AI-specific threats
- Designed for CISOs, security directors, enterprise architects, and professionals responsible for AI governance
- Features expert-led sessions and panel discussions with no product presentations during talks
- Attendees can earn continuing education credits (CPE, CEU, PDU, CISSP)
About the Event
AI Security Strategies: Chicago is a one-day conference focused on the security risks that accompany rapid AI adoption in enterprise environments. The event brings together security leaders and technology professionals to examine how organizations can protect AI-enabled systems, manage emerging threats, and establish governance practices that support responsible deployment.
The conference format includes expert-led sessions and a panel discussion. Product presentations are excluded from the main program, with networking opportunities available during breaks and lunch for those interested in connecting with sponsors offering AI security solutions.
Core Topics
Sessions address AI security strategy development, data protection for AI systems, and vulnerabilities unique to AI environments. The program also covers governance frameworks, monitoring practices, and operational controls specific to AI deployments. Attendees will learn methods for measuring AI security readiness and maturity within their organizations.
A recurring theme throughout the event is translating lessons learned from security leaders who have navigated AI transformation into actionable guidance for attendees facing similar challenges.
Who Should Attend
The conference targets CISOs, security directors, and security managers alongside IT security, risk management, and compliance professionals. Enterprise architects and those responsible for AI adoption decisions will find relevant content. The event is particularly suited to organizations in sectors adopting AI at scale who need practical frameworks for identifying AI-specific risks, protecting sensitive data processed by AI systems, and establishing appropriate controls.