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Choose the platform that can model your actual AI workflow, put the right people in the same decision process, and turn the exercise record into owned corrective actions. A polished scenario library is not enough. Before buying, make each provider demonstrate one of your incidents and verify how it handles role separation, sensitive documents, access control, retention, exports, and contract terms.
AI incident response requires more than technical containment. A model may depend on data pipelines, cloud identities, retrieval stores, plug-ins, human reviewers, vendors, and business processes. An exercise must therefore test information sharing, escalation, and operational coordination alongside investigation and recovery.
What an incident-response tabletop platform actually does
NIST defines a tabletop exercise as “A discussion-based exercise where personnel with roles and responsibilities in a particular IT plan meet in a classroom setting or in breakout groups to validate the content of the plan by discussing their roles during an emergency and their responses to a particular emergency situation. A facilitator initiates the discussion by presenting a scenario and asking questions based on the scenario.”
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For an AI workflow, the scenario should represent the system as it operates in production. That includes the model or models, training or retrieval data, prompts and policies, connected tools, cloud services, identity controls, human approvals, external providers, and the business activity that depends on the output. The exercise should also represent consequences that may not look like a conventional server compromise, such as unsafe or misleading outputs, loss of confidentiality through a connected service, disruption of an automated process, or uncertainty about whether an output can be trusted.
The 2024 Joint Cyber Defense Collaborative AI tabletop exercise organized by the Cybersecurity and Infrastructure Security Agency (CISA) illustrates the broader scope. Its objectives covered information-sharing opportunities, response procedures for multistage incidents, gaps in plans, and operational collaboration around AI-enabled systems. Those are exercise objectives, not a measured effectiveness score, but they are a useful test of whether a product treats AI response as a cross-functional problem.
Set the selection criteria before you see a sales demonstration
1. AI-specific scenario coverage
Ask whether you can describe the complete workflow rather than selecting a generic “AI attack” label. The platform should let you identify:
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- what the AI system does for the business and which decisions or services depend on it;
- models, data stores, retrieval components, agents, plug-ins, APIs, cloud accounts, and other dependencies;
- people who approve, monitor, operate, or consume the system;
- third parties that host, supply, maintain, or receive data from the workflow;
- technical, legal, safety, privacy, financial, and reputational effects of an incident; and
- multiple stages, such as initial discovery, attempted containment, a new development, and recovery.
Test whether the facilitator can change the scenario when participants make an unexpected decision. A fixed presentation may be useful for orientation, but it will not reveal how your plan behaves when a dependency is unavailable, facts are disputed, or an executive chooses a different risk trade-off.
2. Cross-functional decision practice
Include security operations, engineering, the AI or data-science owner, the business owner, legal and privacy advisers, communications, executive leadership, procurement or vendor management, and relevant external partners. The product should support information exchange between those roles without assuming that everyone may see every document.
Ask practical questions: Can a participant acknowledge an inject without receiving privileged material? Can legal hold a public statement while engineering continues containment? Can a vendor representative provide facts without becoming an incident commander? Can the facilitator see which team is waiting for another team’s decision?
3. Facilitation and role design
Look for controls that let the exercise lead:
- start, pause, accelerate, or reorder injects;
- send different facts or questions to specific roles;
- introduce new evidence after a decision;
- record the time and owner of a decision;
- capture dissent, assumptions, and requests for information; and
- keep an authoritative timeline while participants collaborate.
Some provider pages describe role-specific views and live facilitator controls. Treat those descriptions as claims to demonstrate, not as independently verified results. Have the facilitator run a short, unscripted segment during the demonstration.
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4. After-action quality
The exercise is only useful if the record supports work after the meeting. Require a sample report showing:
- the scenario, objectives, participants, and timeline;
- decisions, decision owners, and the information available at the time;
- gaps in procedures, authority, data, staffing, or technology;
- actions with an accountable owner, due date, priority, and status;
- areas where participants disagreed or relied on an unverified assumption; and
- export formats that your governance, ticketing, or risk systems can use.
A web or PDF report can be a convenient deliverable, but format alone does not make observations actionable. Ask to see how an observation becomes a tracked improvement and how a later exercise verifies that it was fixed.
5. Handling of sensitive inputs
AI exercises often use incident-response plans, playbooks, policies, network diagrams, vendor details, and architecture documents. Before uploading anything, establish:
- where files and exercise data are processed and stored;
- which provider staff, subcontractors, or other tenants can access them;
- whether submitted content is used to train or improve a shared model;
- how encryption, authentication, logging, and tenant separation work;
- retention periods, backup handling, deletion timing, and legal holds;
- how you can export the complete record and verify deletion; and
- which contractual commitments, audit reports, and breach-notification terms apply.
A product page that invites you to upload plans does not, by itself, establish that these controls exist. Have your security team and counsel review the answers before providing sensitive material.
6. Fit with your operating model
Confirm participant access requirements, identity-provider integration, supported browsers and devices, accessibility features, exercise cadence, deployment model, regional processing options, and support hours. Check whether the platform works for one team or for multiple business units with separate permissions and reporting. Obtain current pricing and licensing details directly; the available product descriptions do not establish comparable prices.
Platforms and approaches to put on your shortlist
The following options are not ranked. Their capabilities below come from provider descriptions or, in one case, search-result material. None of those descriptions is independent evidence of security, effectiveness, or exercise outcomes.
| Option | What it describes | Questions to verify | Potential fit |
|---|---|---|---|
| TableTop Sim | Role-specific incident injects, facilitator controls, tailoring to a client environment, and web or PDF after-action reports. Its examples include ransomware, business email compromise, malicious insider, third-party compromise, cloud identity takeover, and data extortion. | Demonstrate how an AI workflow, its dependencies, and a multistage decision path are represented. Verify storage, access, retention, export, and contractual controls. | Teams wanting a hosted exercise interface with facilitator-led injects and formal reports, subject to demonstration and security review. |
| ORNA | AI cyber-crisis simulations, generated after-action review reports, and customization using uploaded incident-response plans, playbooks, policies, and network diagrams. The page describes an integrated cloud platform. | Show exactly what uploaded documents are retained, who can access them, whether they enter model training, and how deletion is proven. Test whether generated findings are editable, attributable, and assignable. | Organizations interested in document-informed scenario customization that can satisfy the provider’s data-handling requirements. |
| Tabletop.ai | Live exercises with role-specific participation, control mapping, generated after-action reports, and scorecards. | The available description was not independently opened, so verify every capability directly, including the meaning of “control mapping,” report exports, and access separation. | Teams seeking control-oriented reporting, after direct product verification. |
| DIY using CISA materials | CISA exercise materials and programs provide objectives, scenarios, and discussion questions. They can be used without a platform license. | Confirm that the selected package applies to your sector, adapt it to your AI workflow, provide a facilitator, and create your own secure record and action-tracking process. | Organizations with facilitation capacity or a small budget that need a credible starting point rather than a managed software service. |
No independent side-by-side test or comparative outcome data establishes that one of these approaches produces better incident response. Select on demonstrated fit and verified controls, not on the presence of an AI label.
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Run the same evaluation exercise with every provider
A standardized demonstration prevents a vendor from steering the conversation toward its strongest feature. Prepare a short brief that is realistic but sanitized:
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- the model, data sources, connected services, identities, tools, and third parties involved;
- the roles that must make or approve decisions;
- the facts participants may share and the material that must remain restricted; and
- constraints such as an unavailable administrator, a reporting deadline, or a requirement to preserve evidence.
- Send identical requirements. Give each provider the same brief, participant list, time limit, and requested outputs. Do not let one provider substitute a generic scenario.
- Watch scenario construction. Ask the provider to map the workflow and identify assumptions before starting. Note what it cannot represent.
- Test role-specific information. Have security, engineering, legal, communications, and the business owner join with deliberately different permissions. Check whether each person receives enough information to act without seeing material outside their role.
- Force an unexpected branch. Change a key fact halfway through: for example, make a dependency unavailable or reveal that the initial indicator was incomplete. Observe whether the facilitator can adapt without losing the timeline.
- Inspect the record. Require the final exercise record, decision log, unresolved questions, and action list. Ask how an action is assigned, notified, exported, and later closed.
- Interrogate data handling. Request a diagram of processing and storage, the list of subprocessors, retention and deletion behavior, access logs, and relevant contract language. Do not accept a marketing label as proof of a legal privilege or compliance outcome.
- Score against your plan. Record missing dependencies, unclear authority, inaccessible data, and manual workarounds. A platform that exposes a weakness clearly may be more valuable than one that produces a polished but shallow report.
How to run an AI incident tabletop once you select a platform
Prepare the exercise
Define two or three learning objectives, such as validating who can suspend an automated workflow, testing escalation to a model provider, or agreeing what evidence must be preserved. Map the AI workflow and its dependencies before writing injects. Decide which details are fictionalized and which are drawn from current architecture. Assign an exercise director, facilitator, technical lead, scribe, and observers.
Brief participants
Explain that the exercise is discussion-based, not a technical penetration test or an individual performance review. State the starting facts, the time horizon, the communication channels, and any information-sharing restrictions. Give participants the authority boundaries that apply in a real incident; otherwise the exercise may hide the very approval bottlenecks you need to find.
Deliver a multistage scenario
Begin with a credible signal and ask each role what it knows, what it needs, and what it will do next. Add developments that test the AI workflow and the organization around it: a dependency stops responding, a third party supplies conflicting information, a business process continues using questionable output, or leadership must choose between speed and additional verification. Keep injects tied to the stated objectives rather than adding drama without a decision to make.
Capture decisions as they happen
For every consequential decision, record the time, decision-maker, alternatives considered, evidence available, assumptions, and follow-up owner. Record requests that cannot be answered during the exercise. This distinguishes a genuine plan gap from a participant who simply lacked information in the moment.
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Separate observations from proposed fixes. Rank actions by risk and dependency, assign accountable owners and due dates, and state how completion will be verified. Schedule a short re-test for high-priority changes; otherwise the report can become an archive rather than an improvement cycle.
Security and procurement questions to put in writing
| Area | Questions | Evidence to request |
|---|---|---|
| Data flow | What content is uploaded, generated, logged, or sent to subprocessors? In which countries is it processed? | Current architecture and data-flow description; subprocessor list. |
| Model use | Is customer content used for training, tuning, analytics, or human review? Can those uses be disabled? | Contract terms and product settings, not a verbal assurance. |
| Access | How are facilitators, participants, observers, and provider administrators separated? Is single sign-on and multi-factor authentication supported? | Role matrix, authentication documentation, and access logs or samples. |
| Retention | How long are plans, transcripts, recordings, reports, backups, and deleted accounts retained? | Retention schedule and deletion procedure, including backup treatment. |
| Export | Can you export injects, messages, decisions, files, timestamps, and action status in a usable format? | A complete sample export and documented deletion confirmation. |
| Availability | What happens if the service is unavailable during an exercise or an incident? Can you run or retrieve a record offline? | Business-continuity commitments and recovery procedures. |
| Legal and compliance | What contractual commitments apply to confidentiality, breach notification, audit rights, and data location? | Applicable agreement, security addendum, and counsel review. |
A practical scorecard for the final decision
Use a written scorecard so a compelling demonstration does not outweigh a serious data-handling gap. The percentages below are an example weighting; adjust them to your risk and operating model.
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| Criterion | Example weight | What earns a high score |
|---|---|---|
| AI workflow and multistage scenario fidelity | 25% | Represents dependencies, business impact, uncertainty, and changing facts without forcing a generic IT scenario. |
| Cross-functional participation | 20% | Supports secure, role-appropriate information sharing and clear escalation among technical, business, legal, communications, leadership, and partners. |
| Facilitation and adaptability | 15% | Allows live control of injects, branching, timing, permissions, and decision capture. |
| After-action and improvement workflow | 15% | Produces an auditable timeline and assignable, exportable actions that can be re-tested. |
| Security, privacy, and contractual fit | 20% | Provides verifiable answers on processing, access, retention, deletion, export, subprocessors, and incident obligations. |
| Operational fit and cost | 5% | Meets access, accessibility, integration, deployment, support, cadence, and budget requirements after current terms are confirmed. |
Set minimum gates rather than allowing a high total score to excuse a failure. For example, a provider that cannot explain deletion of uploaded network diagrams should not pass merely because its scenario editor is attractive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common selection mistakes and how to correct them
Choosing a generic scenario library
Symptom: The exercise mentions an “AI breach” but never identifies the model’s data, tools, owners, or business consequence.
Fix: Supply your workflow map and require the provider to show where each dependency and decision appears in the scenario.
Testing only the security team
Symptom: Analysts can investigate, but nobody practices customer messaging, legal review, executive risk acceptance, or vendor escalation.
Fix: Include every role that can authorize containment, continue operations, disclose information, or accept residual risk.
Accepting a generated report without ownership
Symptom: The report lists observations but no accountable person, due date, priority, or verification method.
Fix: Require an action workflow and sample export during procurement; assign owners before the meeting ends.
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Uploading sensitive architecture too early
Symptom: A team sends playbooks or diagrams to a trial account before understanding retention or model-use terms.
Fix: Start with sanitized material, complete security and contract review, then provide the minimum necessary detail.
Confusing a platform feature with assurance
Symptom: A provider’s “secure,” “compliant,” or “AI-powered” wording is treated as proof of a control or legal result.
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Fix: Ask for the underlying configuration, contract language, and independent evidence where applicable. Have counsel assess privilege and compliance claims.
Running an exercise with no follow-through
Symptom: Participants attend once, receive a PDF, and never revisit the identified gaps.
Fix: Put corrective actions into the same governance process used for other risks and schedule a focused re-test for important changes.
When a DIY exercise is the better choice
CISA materials can provide a no-cost starting point for an organization that can supply a facilitator, protect its own documents, and tailor the scenario. Use the objectives, scenario material, and discussion questions as a foundation, then add your AI workflow, dependencies, role permissions, and business decisions. The cited CISA program page is sector-specific, so confirm that the package fits your organization before adopting it unchanged.
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Final decision rule
Select the platform that can faithfully represent your AI workflow, let the right people exchange the right information under pressure, and leave you with a secure, exportable record of decisions and owned improvements. If a provider cannot demonstrate those three outcomes with your scenario—or cannot answer how your uploaded material is processed and deleted—do not proceed on the strength of a feature page. A well-facilitated exercise using tailored CISA materials is preferable to a sophisticated interface that obscures authority, dependencies, or unresolved risk.
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