An AI incident response plan should tell people how to recognize a harmful or potentially harmful event, who has authority to act, how to limit impact, what evidence to preserve, how to support affected people, and how to restore or retire the system safely. It should fit the organization’s AI systems and local legal obligations; it is an operational plan, not a universal compliance template.
What counts as an AI incident?
Set a shared threshold before an event occurs. The OECD distinguishes an AI incident, involving actual harm, from an AI hazard, a condition or event with the potential to cause harm. A plan can also define near misses and severity levels so teams know when to investigate, escalate, or take a system out of service. These terms and their application may vary by jurisdiction and context; see the OECD’s definitions of AI incidents and related terms.
Specify the systems and events in scope, including relevant third-party models, APIs, data services, and downstream uses. Consider harmful or unreliable outputs, safety failures, privacy or security events, misuse, unfair outcomes, material performance changes, and failures in dependencies. State exclusions and escalation routes rather than assuming every AI-related concern is the same kind of emergency.
What should the plan include?
Purpose, scope, and system context
Describe the systems covered, their intended uses, responsible business units, deployment context, and important upstream or downstream dependencies. Keep an inventory that responders can quickly consult: system owner, model and version, relevant data and configuration, documentation, implementation or code references where appropriate, response plan, and contact details. The NIST AI RMF Playbook offers voluntary suggestions for inventory and documentation.
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Roles, authority, and contacts
Name an incident lead and an accountable decision-maker, then identify technical and AI system owners, security, privacy, legal or compliance, business operations, communications, vendors, and alternates. Make clear who can restrict use, route decisions to human review, override or roll back a model, pause integrations, or decommission a system—and who can approve return to service. NIST recommends defining personnel responsible for monitoring and incident response and maintaining relevant actor contact information.
Detection, intake, and triage
List the monitoring signals and thresholds that can trigger review, plus reporting channels for staff, users, vendors, and affected people or communities. Assign case ownership and escalation paths. For uncertain or high-impact outcomes, provide human adjudication rather than treating a model’s output as self-validating.
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For each report, record whether an AI system may have caused or amplified the event, what is known and uncertain, and the likely impact. Assess severity, affected people and groups, scale and duration, safety, privacy, security and fairness implications, reversibility, and downstream reliance. Record why the team assigned its severity level and chose its response. These are practical decision factors, not an official scoring rubric.
Containment and evidence preservation
Define response options in advance and match them to the system’s architecture and potential harm. Depending on the event, responders may isolate credentials or integrations, restrict a feature or affected use, pause automated decisions, route cases to human review, invoke appeal or override processes, roll back a change, or deactivate the system. Set decision criteria and authority for each option. Preserve relevant evidence before changing the system where feasible, without delaying an urgent step needed to protect people.
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Maintain a timestamped event record. Depending on what is lawful and necessary, it may include relevant inputs and outputs, logs, system and model versions, configuration changes, affected records, the decision rationale, communications, and containment actions. Limit access and track custody of sensitive evidence. NIST recommends processes to track and document incident response and recovery.
Communication and recourse
Specify who receives internal escalations, how vendors are contacted, and who coordinates notices to users, customers, affected communities, regulators, or the public. Provide channels for people to report problems, contest outcomes, request human review, and learn what recourse is available. Where appropriate, identify an alternative process or opt-out route. NIST guidance calls for incident and error communications to reach relevant AI actors and affected communities.
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Include a legal or compliance review before deciding whether a notification is required and when it is due. Reporting duties depend on jurisdiction, sector, system use, and the event’s facts. The OECD’s common reporting framework is a benchmark intended to be adaptable to domestic policy and legal frameworks; it does not create one universal reporting duty for every organization.
Recovery, validation, and return to service
Identify a safe fallback process for when the AI system is restricted or unavailable. Before restoring service, set the checks, monitoring, and approval needed to establish that the cause has been addressed and residual risks are acceptable. Document who accepts those risks and what conditions require continued suspension, further changes, or decommissioning. NIST’s AI Risk Management Framework connects incident response and recovery with post-deployment monitoring, override, decommissioning, and change management.
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After-action review and plan maintenance
Review root and contributing causes, harms and unresolved impacts, whether controls worked, and what corrective actions are needed. Assign owners and due dates; update the system inventory, risk assessment, monitoring, and response procedures. Where appropriate, consult affected stakeholders. Exercise the plan with scenarios that test contact paths, vendor escalation, evidence capture, rollback or restoration, and communication approvals. Set a review owner and cadence, and revisit the plan after system or model changes and incidents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should an organization set response priorities?
A workable severity scheme weighs the potential harm and its seriousness; how many people are affected and their vulnerability; safety, privacy, security, fairness, and service continuity; duration, reach, reversibility, and downstream reliance; confidence that the AI system caused or amplified the event; and any applicable reporting duties. Use those factors to define thresholds that fit the organization’s systems and context rather than borrowing a generic score without validation.
The OECD’s 2025 common reporting framework contains 29 criteria and aims to help organizations and policymakers understand incidents across contexts, identify high-risk systems, assess current and emerging risks, and evaluate effects on people and the planet. That framework can inform consistent recordkeeping, but it does not substitute for local legal review.
What guidance can help shape the plan?
NIST’s AI Risk Management Framework (AI RMF) is voluntary and lifecycle-oriented. Its Manage function states: “Risk treatment comprises plans to respond to, recover from, and communicate about incidents or events.” The NIST AI RMF Core provides the framework’s outcomes, while the Playbook suggests actions rather than prescribing a checklist or mandatory sequence. NIST released AI RMF 1.0 on January 26, 2023; its status page says the framework is being revised. NIST released a Generative AI Profile on July 26, 2024, which can help identify generative-AI-specific risks. On April 7, 2026, it released a concept note for a critical-infrastructure profile; that is a concept note, not a final sector rule. See the NIST AI Risk Management Framework status page for current status.
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The OECD published its proposed incident and related-term definitions on May 6, 2024, and its common reporting framework on February 28, 2025. Together, these sources can help organizations structure definitions and records, while the plan itself must reflect the system, deployment, and applicable local requirements.
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