This workshop examines how AI systems use context and why that fundamentally reshapes identity and trust.
• AI systems operating across environments and services
• MCP as the foundation for context exchange
• Identity, trust, and governance impacts
• Hands-on exploration of context and identity signals
• Shifting trust boundaries and emerging risks
• Practical focus on real-world implementation
Key Takeaways
A working, implementation-level understanding of Model Context Protocol (MCP)
A clearer mental model of how AI systems request, exchange, and operationalize context
Insight into how identity signals surface within AI-to-system interactions
Awareness of how dynamic context assembly alters traditional trust boundaries
Practical exposure to MCP-based interactions through guided lab exercises
Improved ability to assess governance, risk, and architectural implications of AI identity systems
Actionable understanding of how context-driven AI architectures impact enterprise and consumer environments
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