How to Run an AI Governance Tabletop Exercise for a University
A practitioner-level methodology for testing whether people, policies, and runtime controls can detect, contain, and respond to an AI agent failure before unauthorized access, data exposure, or policy violation occurs.
An AI governance tabletop exercise is a structured, discussion-based simulation that tests whether a university's people, policies, and runtime controls can detect, contain, and respond to an AI agent failure before it results in unauthorized access, data exposure, or policy violation. It requires defined objectives, cross-departmental stakeholder roles, realistic AI-specific scenario injects, and a process for converting findings into governance and control changes.
What an AI Governance Tabletop Exercise Is
An AI governance tabletop exercise is a structured, discussion-based simulation. Participants walk through an AI agent failure scenario and examine whether institutional people, policies, and runtime controls can detect the event, contain its impact, and respond before the situation becomes unauthorized access, data exposure, or a policy violation.
Unlike a generic IT tabletop, the focus is AI-specific: agent behavior, tool use, permission scope, and the governance decisions that determine whether an incident stays contained. The exercise is most useful when it ends with concrete governance and control changes, not only a record of discussion.
Exercise Structure
A complete exercise typically covers five connected parts. Define them in order so scenarios test a real capability rather than a vague theme.
- Scope and objectives Define what capability is being tested before writing scenarios.
- Stakeholder roles Assign decision authority across IT, legal, the provost's office, research compliance, and procurement.
- Scenario injects Build AI-specific failure modes, not generic IT security scenarios.
- Runtime validation Test agent identity, permissions, policy enforcement, and audit logging.
- After-action mapping Convert findings into governance and control changes.
Defining Scope and Objectives Before Scenario Design
Scope and objectives come first. Before drafting injects, decide which capability the university is testing: detection, containment, escalation, policy interpretation, cross-department coordination, or a combination of these. A first AI-specific exercise should stay narrow enough to finish in a single session.
Stakeholder roles should match real decision authority. Typical participants include IT security, legal, the provost's office, research compliance, and procurement. Each role needs a clear mandate so the discussion surfaces ownership gaps rather than hypothetical consensus.
Scenario injects should reflect AI agent failure modes. Unauthorized tool calls, over-broad permission use, unexpected data access, and policy bypass paths are more useful than generic outage or phishing narratives. The inject should force participants to answer who can stop the agent, under what policy, and with what evidence.
Runtime Governance Controls to Validate, Not Just Discuss
The value of a tabletop exercise depends on whether it tests actual controls rather than assumed ones. NIST SP 800-53 Rev. 5 provides a relevant baseline through its Access Control (AC) and Audit and Accountability (AU) control families. During a simulated unauthorized tool-call scenario, the exercise should ask concrete questions:
- Can the university confirm which identity the AI agent was operating under?
- Was that agent's permission scope actually limited to what its task required?
- Did a policy enforcement mechanism intervene?
- Does an audit log exist that can reconstruct the sequence of actions afterward?
These four elements (agent identity, least-privilege permissions, policy enforcement, and audit logging) are the runtime governance concepts the exercise should validate as tested capabilities rather than assumed protections.
Agent identity
Confirm which identity the AI agent operated under during the scenario.
Least-privilege permissions
Verify permission scope matched the task, not a broader standing grant.
Policy enforcement
Determine whether an enforcement mechanism intervened in time.
Audit logging
Confirm logs can reconstruct the sequence of agent actions afterward.
How Trussed AI maps to the exercise
Trussed AI provides runtime governance and security controls for AI agents, including agent identity, least-privilege permissioning, tool approval workflows, and audit logging. These are the categories of control this type of exercise is designed to exercise and verify.
Translating After-Action Findings into Governance Changes
Findings only matter if they become changes. After the session, map each gap to a governance owner, a control adjustment, or a policy revision. Runtime validation gaps (identity, permissions, enforcement, logging) should not remain discussion notes; they should become implementation work with clear accountability.
Use the after-action process to distinguish assumed protections from proven ones. If participants could not show who the agent was, what it was allowed to do, whether policy stopped it, or how the path would be reconstructed, treat those as control failures to remediate before the next exercise or a live incident.
Common Questions
How long should the exercise take?
CISA's CTEP format is designed for discussion-based sessions, typically ranging from two to four hours depending on scenario complexity and the number of departments involved. A university's first AI-specific exercise should be scoped narrowly enough to complete in one session.
How often should the exercise be repeated?
There is no single standard cadence required for every institution. A reasonable approach is to repeat the exercise when new AI tools are deployed, after significant findings from a prior exercise are remediated, or on a recurring annual basis aligned with other institutional risk assessments.
Who should facilitate the exercise?
CISA's guidance places facilitation with a neutral party using a facilitator guide and situation manual, rather than a stakeholder with a direct decision-making role in the scenario. This keeps IT security, legal, and the provost's office focused on their response roles rather than running the exercise.
Validate Runtime Governance Before an Incident Tests It for You
Tabletop exercises identify governance gaps. Runtime governance controls close them by enforcing agent identity, least-privilege permissions, and auditability in production.
Explore Runtime Governance