AI Incident Response Checklist

The first AI incident arrives before the response process exists unless the process is written first. Agents act, models change, content injects, and someone asks what happened and why. This checklist covers what must be prepared and what to do: detection, containment, reconstruction, remediation, disclosure, root cause, and prevention. It complements ai incident response, ai incident disclosure, and the agentic AI governance whitepaper. This is general guidance, not legal advice.

Who should use this checklist?

Engineering and business owners of AI systems, security and incident response teams, and governance functions responsible for AI risk.

Preparation: are incidents defined and detectable?

1. Incident definition and severity levels are written for AI systems, covering unauthorized actions, leakage, harmful outputs, injection, provider incidents, and quality regressions.
2. Detection is instrumented: validation failures, blocked actions, anomaly alerts, quality drops, user and reviewer reports, provider notifications.
3. Reporting channels for users and reviewers exist and are known.
4. On-call and escalation paths include the business owner and security.

Reference: the AI observability whitepaper.

Preparation: are containment actions pre-authorized?

Action	Pre-authorized by	Mechanism tested
Drop autonomy level to suggest-only	Business owner and engineering owner	Configuration change
Disable the agent or feature	Engineering owner	Feature flag or gateway route
Revoke or rotate credentials	Security	Secrets manager
Block specific tools or destinations	Engineering owner	Tool registry policy
Roll back to previous approved version	Engineering owner	Registry pointer
Pause affected workflows and notify reviewers	Business owner	Queue controls

Reference: how to build tool use for llm agents and how to build an llm gateway.

Preparation: does the audit trail support reconstruction?

1. Trajectory records capture inputs, context, model and prompt versions, tool calls, validation and gate decisions, approvers, and effects.
2. Records are correlated by identifiers across layers.
3. The trail is append-only, retained, and access-controlled.
4. Reconstruction drills have been run on random cases.

Reference: how to build an ai audit trail.

Preparation: are obligations mapped?

1. Notification and disclosure obligations by data type, sector, and jurisdiction are documented.
2. Vendor notification terms with model providers and partners are known.
3. Communications templates for users, customers, and regulators are prepared.
4. Legal and privacy contacts are named.

Reference: ai data privacy compliance.

Response: detect and triage

1. Confirm the signal and classify severity against the definition.
2. Name the incident lead and assemble the team.
3. Open the incident record with timeline.
4. Decide immediate containment level.

Response: contain

1. Execute pre-authorized containment proportionate to severity.
2. Confirm containment through monitoring (no further out-of-spec actions).
3. Preserve evidence: do not alter logs or affected records beyond containment.
4. Notify stakeholders per the escalation path.

Response: reconstruct and assess

1. Reconstruct affected trajectories from the audit trail.
2. Determine scope: which cases, users, data, and actions were affected, and over what period.
3. Assess harm: financial, data exposure, customer impact, regulatory exposure.
4. Identify model, prompt, and configuration versions involved and whether a provider change occurred.

Response: remediate and notify

1. Reverse affected actions where possible; correct records; compensate or remediate customers per policy.
2. Notify affected parties and regulators per mapped obligations and legal advice.
3. Notify vendors where their components are implicated.
4. Document all remediation in the incident record.

Response: root cause

Category	Questions
Specification	Was the behavior out of spec, or was the spec incomplete?
Permissions	Did the agent hold more capability than the spec required?
Content and injection	Did untrusted content influence behavior?
Model and provider	Did a model or provider change occur without re-evaluation?
Data and retrieval	Did stale, wrong, or unauthorized content reach the model?
Validation and gates	Did checks fail, or were they missing? Was review ceremonial?
Operations	Were alerts missed, or runbooks absent?

Reference: the AI agent security architecture whitepaper and why ai agents fail in production.

Prevention: close the loop

1. Add the incident's cases to the golden dataset and adversarial suite.
2. Change specification, permissions, validation, or gates as root cause indicates.
3. Adjust autonomy level and the evidence required to restore it.
4. Update runbooks, detection, and training.
5. Report to the governance body and update the register.
6. Verify fixes through evaluation before restoring autonomy.

Reference: how to build an agent evaluation harness.

Exercise: has the playbook been rehearsed?

1. A tabletop exercise with an AI scenario has been run with the full team.
2. Containment mechanisms have been executed in a controlled test.
3. Reconstruction has been performed on a simulated incident.
4. Lessons from the exercise are incorporated.

How should this checklist be used?

Preparation items are completed before launch and reviewed quarterly. Response items are the runbook during an incident. Prevention items are completed before the affected system's autonomy is restored. The incident record is the evidence for governance and, where applicable, regulators.

How FISTA Solutions prepares for AI incidents

FISTA Solutions delivers every AI system with the preparation this checklist requires: incident definitions, instrumented detection, pre-authorized and tested containment through the gateway, tool registry, and registry rollback, an audit trail built for reconstruction, and a rehearsed playbook. The AI enablement practice provides the platform controls, AI agents are delivered with containment paths, and forward deployed engineers run tabletop exercises with your teams. The record behind the approach is 150+ projects with 99.9% uptime.

This checklist is general guidance, not legal advice. To prepare or exercise an AI incident response plan, message FISTA on WhatsApp, or read ai agent security risks for the threats it addresses.