Most AI governance programs ask 'how do we prevent this from going wrong?' Almost none ask 'when this goes wrong, who specifically is on the hook?' The first question produces frameworks. The second question produces lived accountability. This session presents black swan scenario modeling as a structured exercise that forces enterprises to confront ownership BEFORE an incident, not after.
Drawing on the speaker's 15-plus years across regulated industries and her tenure as Chief Compliance Officer at a publicly traded payments technology company, this session walks through how black swan modeling reveals what documented governance hides.
The exercise is simple to describe and uncomfortable to execute. For a specific high-stakes AI use case, the team is asked to imagine the worst-case incident: the AI tool discriminates at scale, the agentic system takes an unauthorized action with material consequence, the model produces a defamatory output that ends up in the news. Then the team is asked: who specifically is on the hook? Whose name appears on the press release? Who explains this to the board? Who, by name, gets fired if this happens?
When the team cannot answer that question cleanly, the governance program has a documented-but-not-lived gap. When they can answer it, the next question is whether that person actually has the authority and the information to prevent the worst case. If not, accountability is decoupled from authority, and the program is set up to scapegoat the named owner for failures they could not prevent.
The exercise produces several outputs:

Pre-assignment of incident response ownership at the use case level.
Identification of decision-rights gaps between accountability and authority.
Lived ownership confirmation: the named owner either accepts the worst-case scenario at their desk or pushes back, triggering a governance redesign.
Audit trail evidence that the organization considered worst-case scenarios before deploying, which is itself a defensibility asset.

Specific topics covered:

How to construct credible black swan scenarios that survive executive scrutiny.
The 'who gets fired' question as a forcing function for honest ownership conversations.
The connection between black swan exercises and the broader lifecycle decision-rights architecture.
How black swan modeling produces evidence that supports defensibility when something does go wrong.

Learning objectives:

Design black swan scenarios that stress-test AI governance ownership.
Use worst-case scenario modeling to surface documented-vs-lived decision-rights gaps.
Produce pre-incident accountability assignments that hold under examination.
Build the audit trail that supports defensibility after an incident.

Practitioner-derived frameworks, broadly applicable.
For organizers: panel placement welcome.