Organizations are rapidly deploying AI systems that recommend, decide, communicate, and act—but many cannot reconstruct how a consequential output was produced. When an AI decision is challenged by a customer, regulator, executive, court, or incident-response team, model accuracy is no longer enough. The organization must be able to identify the data, model version, prompt, agent action, policy, and human approval that shaped the result.

This panel brings together Daniel Garrie’s experience as an AI dispute-resolution architect, forensic technologist, attorney, and court-appointed neutral with the perspective of a senior applied-AI leader. Using realistic failure scenarios from energy, finance, healthcare, and the public sector, the panel will examine data lineage, model and prompt provenance, agent logging, human oversight, access controls, testing, retention, and AI incident response.

Attendees will leave with a practical AI Evidence Readiness Framework explaining what organizations should preserve, who should own it, how long it should be retained, and how teams can test whether their AI audit trail actually works before an incident occurs.