Who Dunnit: AI Identity & Action Attribution

About this session

As AI agents move from assistants to autonomous actors, sending messages, executing transactions, and changing data, organizations must answer a critical question: when an agent acts, who is accountable?

Consider a seemingly simple request: “clean up my inbox.” If an agent deletes hundreds of emails, including records subject to retention requirements, was that action taken under the user’s authority, the agent’s interpretation, or the platform’s configuration? In many cases, organizations can determine what happened, but struggle to prove who authorized it and why.

This session introduces a practical framework for AI action attribution, built around the concept of an Action Provenance Chain: capturing who did what, under whose authority, and why, at the moment of execution. Attendees will leave with a clear model for improving auditability, reducing attribution gaps, and designing accountable, trustworthy AI systems in an increasingly agent-driven world.

Speaker

• Ruchi Batra, Information Security Director, U.S. Bank

Key takeaways

• A common vocabulary for discussing AI accountability
• A framework for designing attribution into AI systems from the start.
• Actionable guidance to improve trust, auditability, and governance in agent-enabled environments

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